Publications | Prof. Dr. Fabian Gieseke | Department of Information Systems

2025

Research article in proceedings (conference)

Fayad, I., Zimmer, M., Schwartz, M., Ciais, P., Gieseke, F., de Truchis, A., Brood, S., Belouze, G., & d'Aspremont, A. (2025). DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications. In Proceedings of the 42nd International Conference on Machine Learning (ICML), Vancouver. (accepted / in press (not yet published))
More details BibTeX

@inproceedings{FayadZSCGDTBBDA2025DUNIAPixelSized,
  author = {Ibrahim Fayad and Max Zimmer and Martin Schwartz and Philippe Ciais and Fabian Gieseke and Aurélien {de Truchis} and Sarah Brood and Gabriel Belouze and Alexandre d'Aspremont},
  title = {DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications},
  booktitle = {Proceedings of the 42$^{nd}$ Conference on Machine Learning (ICML)},
  year = {2025},
  address = {Vancouver},
  note = {Publication status: accepted / in press (not yet published)}
}

Pauls, J., Zimmer, M., Turan, B., Saatchi, S., Ciais, P., Pokutta, S., & Gieseke, F. (2025). Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation. In Proceedings of the 42nd International Conference on Machine Learning (ICML), Vancouver. (accepted / in press (not yet published))
More details BibTeX

@inproceedings{PaulsZTSCPG2025CapturingTemporal,
  author = {Jan Pauls and Max Zimmer and Berkant Turan and Sassan Saatchi and Philippe Ciais and Sebastian Pokutta and Fabian Gieseke},
  title = {Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation},
  booktitle = {Proceedings of the 42$^{nd}$ International Conference on Machine Learining (ICML)},
  year = {2025},
  address = {Vancouver},
  note = {Publication status: accepted / in press (not yet published)}
}

Research article (journal)

Bernardino, P. N., Keersmaecker, W. D., Horion, S., Kerchove, R. V. D., Lhermitte, S., Fensholt, R., Oehmcke, S., Gieseke, F., Meerbeek, K. V., Abel, C., Verbesselt, J., & Somers, B. (2025). Predictability of abrupt shifts in dryland ecosystem functioning. Nature Climate Change, 15, 86–91.
More details BibTeX DOI

@article{BernardinoKHKLFOGMAVS2025Predictability,
  author = {Paulo Negri Bernardino and Wanda De Keersmaecker and Stéphanie Horion and Ruben Van De Kerchove and Stef Lhermitte and Rasmus Fensholt and Stefan Oehmcke and Fabian Gieseke and Koenraad Van Meerbeek and Christin Abel and Jan Verbesselt and Ben Somers},
  title = {Predictability of abrupt shifts in dryland ecosystem functioning},
  journal = {Nature Climate Change},
  year = {2025},
  volume = {15},
  pages = {86--91},
  doi = {10.1038/s41558-024-02201-0},
  note = {Publication status: Published}
}

Brandt, M., Chave, J., Li, S., Fensholt, R., Ciais, P., Wigneron, J.-P., Gieseke, F., Saatchi, S., Tucker, C. J., & Igel, C. (2025). High-resolution sensors and deep learning models for tree resource monitoring. Nature Reviews Electrical Engineering, 2, 13–26.
More details BibTeX DOI

@article{BrandtCLFCWGSTI2025HighResolution,
  author = {Martin Brandt and Jerome Chave and Sizhuo Li and Rasmus Fensholt and Philippe Ciais and Jean-Pierre Wigneron and Fabian Gieseke and Sassan Saatchi and C. J. Tucker and Christian Igel},
  title = {High-resolution sensors and deep learning models for tree resource monitoring},
  journal = {Nature Reviews Electrical Engineering},
  year = {2025},
  volume = {2},
  pages = {13--26},
  doi = {10.1038/s44287-024-00116-8},
  note = {Publication status: Published}
}

2024

Research article in proceedings (conference)

Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2024). CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (Demo Track), Washington, D.C. (accepted / in press (not yet published))
More details BibTeX

@inproceedings{LuelfLMVZG2024CLIPBranches,
  author = {Christian Lülf and Denis Mayr {Lima Martins} and Salles Marcos Antonio Vaz and Yongluan Zhou and Fabian Gieseke},
  title = {CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval},
  booktitle = {Proceedings of the 47$^{th}$ International ACM SIGIR Conference on Research
and Development in Information Retrieval},
  year = {2024},
  address = {Washington, D.C.},
  note = {Publication status: accepted / in press (not yet published)}
}

Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., & Gieseke, F. (2024). Estimating Canopy Height at Scale. In Proceedings of the 41st International Conference on Machine Learning (ICML), Wien. (accepted / in press (not yet published))
More details BibTeX

@inproceedings{PaulsZKSSCPBG2024Estimating,
  author = {Jan Pauls and Max Zimmer and Una M. Kelly and Martin Schwartz and Sassan Saatchi and Philippe Ciais and Sebastian Pokutta and Martin Brandt and Fabian Gieseke},
  title = {Estimating Canopy Height at Scale},
  booktitle = {Proceedings of the 41$^{st}$ International Conference on Machine Learning (ICML)},
  year = {2024},
  address = {Wien},
  note = {Publication status: accepted / in press (not yet published)}
}

Research article (journal)

Oehmcke, S., Li, L., Trepekli, K., Revenga, J. C., Nord-Larsen, T., Gieseke, F., & Igel, C. (2024). Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR. Remote Sensing of Environment, 302.
More details BibTeX Full text DOI

@article{OehmckeLTRNLGI2024DeepPointCloud,
  author = {Stefan Oehmcke and Lei Li and Katerina Trepekli and Jaime C. Revenga and Thomas Nord-Larsen and Fabian Gieseke and Christian Igel},
  title = {Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR},
  journal = {Remote Sensing of Environment},
  year = {2024},
  volume = {302},
  doi = {10.1016/j.rse.2023.113968},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425723005205?via%3Dihub},
  note = {Publication status: Published}
}

2023

Research article in proceedings (conference)

Lima, M., Denis, M. L., Christian;, G., & Fabian, (2023). End-to-End Neural Network Training for Hyperbox-Based Classification. In Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Brügge. (accepted / in press (not yet published))
More details BibTeX Full text

@inproceedings{LimaDCF2023EndToEndNeural,
  author = {Martins Lima and Mayr; Lülf Denis and Gieseke Christian; and  Fabian},
  title = {End-to-End Neural Network Training for Hyperbox-Based Classification},
  booktitle = {31$^{th}$ European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  year = {2023},
  address = {Brügge},
  url = {https://arxiv.org/abs/2307.09269},
  note = {Publication status: accepted / in press (not yet published)}
}

Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2023). Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests. In VLDB, E. (Ed.), Proceedings of the VLDB Endowment (11, pp. 2845–2857). Vancouver: ACM Press.
More details BibTeX Full text DOI

@inproceedings{LuelfLMVZG2023FastSearchByClassification,
  author = {Christian Lülf and Denis Mayr {Lima Martins} and Salles Marcos Antonio Vaz and Yongluan Zhou and Fabian Gieseke},
  title = {Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests},
  booktitle = {Proceedings of the VLDB Endowment},
  pages = {2845--2857},
  volume = {16},
  editor = {Endowment VLDB},
  year = {2023},
  publisher = {ACM Press},
  address = {Vancouver},
  issn = {2150-8097},
  doi = {10.14778/3611479.3611492},
  url = {https://dl.acm.org/doi/abs/10.14778/3611479.3611492},
  note = {Publication status: Published}
}

Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2023). RapidEarth: A Search Engine for Large-Scale Geospatial Imagery. In Proceedings of the ACM SIGSPATIAL 2023, Hamburg. (accepted / in press (not yet published))
More details BibTeX Full text

@inproceedings{LuelfLMVZG2023RapidEarth,
  author = {Christian Lülf and Denis Mayr {Lima Martins} and Salles Marcos Antonio Vaz and Yongluan Zhou and Fabian Gieseke},
  title = {RapidEarth: A Search Engine for Large-Scale Geospatial Imagery},
  booktitle = {Proceedings of the 31$^{st}$ International Conference on Advances in Geographic Information Systems, SIGSPATIAL, Demo Paper, 2023},
  year = {2023},
  address = {Hamburg},
  url = {https://arxiv.org/abs/2309.15617},
  note = {Publication status: accepted / in press (not yet published)}
}

Research article (journal)

Li, , Sizhuo;, B., Martin;, F., Rasmus;, K., Ankit;, I., Christian;, G., Fabian;, N.-L., Thomas;, O., Stefan;, C., Ask, H. J., Samuli;, T., Xiaoye;, d., Alexandre;, C., & Philippe, (2023). Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale. PNAS Nexus, 2(4).
More details BibTeX DOI

@article{LiSMRACFTSASXAP2023DeepLearning,
  author = { Li and Brandt Sizhuo; and Fensholt Martin; and Kariryaa Rasmus; and Igel Ankit; and Gieseke Christian; and Nord-Larsen Fabian; and Oehmcke Thomas; and Carlsen Stefan; and Holm; Junttila Ask and Tong Samuli; and d’Aspremont Xiaoye; and Ciais Alexandre; and  Philippe},
  title = {Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale},
  journal = {PNAS Nexus},
  year = {2023},
  volume = {2},
  number = {4},
  doi = {10.1093/pnasnexus/pgad076},
  note = {Publication status: Published}
}

Reiner, F., Brandt, M., Tong, X., Skole, D., Kariryaa, A., Ciais, P., Davies, A., Hiernaux, P., Chave, J., Mugabowindekwe, M., Igel, C., Oehmcke, S., Gieseke, F., Li, S., Liu, S., Saatchi, S. S., Boucher, P., Singh, J., Taugourdeau, S., Dendoncker, M., Song, X.-P., Mertz, O., Tucker, C., & Fensholt, R. (2023). More than one quarter of Africa's tree cover is found outside areas previously classified as forest. Nature Communications. (accepted / in press (not yet published))
More details BibTeX

@article{ReinerBTSKCDHCMIOGLLSBSTDSMTF2023MoreThanOne,
  author = {Florian Reiner and Martin Brandt and Xiaoye Tong and David Skole and Ankit Kariryaa and Philippe Ciais and Andrew Davies and Pierre Hiernaux and Jerome Chave and Maurice Mugabowindekwe and Christian Igel and Stefan Oehmcke and Fabian Gieseke and Sizhuo Li and Siyu Liu and Sassan S Saatchi and Peter Boucher and Jenia Singh and Simon Taugourdeau and Morgane Dendoncker and Xiao-Peng Song and Ole Mertz and Compton Tucker and Rasmus Fensholt},
  title = {More than one quarter of Africa's tree cover is found outside areas previously classified as forest},
  journal = {Nature Communications},
  year = {2023},
  note = {Publication status: accepted / in press (not yet published)}
}

2022

Research article in proceedings (conference)

Oehmcke, S., & Gieseke, F. (2022). Input Selection for Bandwidth-Limited Neural Network Inference. In Banerjee, A., Zhou, Z.-H., Papalexakis, E. E., & Riondato, M. (Eds.), Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (pp. 280–288). USA: SIAM Publications.
More details BibTeX DOI

@inproceedings{OehmckeG2022InputSelection,
  author = {Stefan Oehmcke and Fabian Gieseke},
  title = {Input Selection for Bandwidth-Limited Neural Network Inference},
  booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
  pages = {280--288},
  editor = {Arindam Banerjee and Zhi-Hua Zhou and  Evangelos E. Papalexakis and Matteo Riondato},
  year = {2022},
  publisher = {SIAM Publications},
  address = {USA},
  doi = {10.1137/1.9781611977172.32},
  note = {Publication status: Published}
}

Oehmcke, S., Li, L., Revenga, J., Nord-Larsen, T., Trepekli, K., Gieseke, F., & Igel, C. (2022). Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass. In Renz, M., & Sarwat, M. (Eds.), 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022) (pp. 1–4). New York, NY, USA: ACM Press.
More details BibTeX DOI

@inproceedings{OehmckeLRNLTGI2022DeepLearning,
  author = {Stefan Oehmcke and Lei Li and Jaime Revenga and Thomas Nord-Larsen and Katerina Trepekli and Fabian Gieseke and Christian Igel},
  title = {Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass},
  booktitle = {30$^{th}$ ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022)},
  pages = {1--4},
  editor = {Matthias Renz and Mohamed Sarwat},
  year = {2022},
  publisher = {ACM Press},
  address = {New York, NY, USA},
  doi = {10.1145/3557915.3561471},
  note = {Publication status: Published}
}

Research article (journal)

Masolele, R. N., De, S. V., Marcos, D., Verbesselt, J., Gieseke, F., Mulatu, K. A., Moges, Y., Sebrala, H., Martius, C., & Herold, M. (2022). Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia. GIScience and Remote Sensing, 59(1), 1446–1472.
More details BibTeX DOI

@article{MasoleleDMVGMMSMH2022UsingHighResolution,
  author = {Robert N. Masolele and Sy Veronique De and Diego Marcos and Jan Verbesselt and Fabian Gieseke and Kalkidan Ayele Mulatu and Yitebitu Moges and Heiru Sebrala and Christopher Martius and Martin Herold},
  title = {Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia},
  journal = {GIScience and Remote Sensing},
  year = {2022},
  volume = {59},
  number = {1},
  pages = {1446--1472},
  doi = {10.1080/15481603.2022.2115619},
  note = {Publication status: Published}
}

Mugabowindekwe, M., Brandt, M., Chave, J., Reiner, F., Skole, D., Kariryaa, A., Igel, C., Hiernaux, P., Ciais, P., Mertz, O., Tong, X., Li, S., Rwanyiziri, G., Dushimiyimana, T., Ndoli, A., Valens, U., Lillesø, J.-P., Gieseke, F., Tucker, C., Saatchi, S. S., & Fensholt, R. (2022). Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda. Nature Climate Change, 13.
More details BibTeX DOI

@article{MugabowindekweBCRSKIHCMTLRDNVLGTSF2022NationWide,
  author = {Maurice Mugabowindekwe and Martin Brandt and Jerome Chave and Florian Reiner and David Skole and Ankit Kariryaa and Christian Igel and Pierre Hiernaux and Philippe Ciais and Ole Mertz and Xiaoye Tong and Sizhuo Li and Gaspard Rwanyiziri and Thaulin Dushimiyimana and Alain Ndoli and Uwizeyimana Valens and Jens-Peter Lillesø and Fabian Gieseke and Compton Tucker and Sassan S Saatchi and Rasmus Fensholt},
  title = {Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda},
  journal = {Nature Climate Change},
  year = {2022},
  volume = {13},
  doi = {10.1038/s41558-022-01544-w},
  note = {Publication status: Published}
}

Revenga, J. C., Trepekli, K., Oehmcke, S., Jensen, R., Li, L., Igel, C., Gieseke, F., & Friborg, T. (2022). Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods. Remote Sensing (Remote Sens.), 14(16), 3912.
More details BibTeX DOI

@article{RevengaTOJLIGF2022AboveGround,
  author = {Jaime C. Revenga and Katerina Trepekli and  Stefan Oehmcke and Rasmus Jensen and Lei Li and Christian Igel and Fabian Gieseke and Thomas Friborg},
  title = {Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods},
  journal = {Remote Sensing (Remote Sens.)},
  year = {2022},
  volume = {14},
  number = {16},
  pages = {3912},
  doi = {10.3390/rs14163912},
  note = {Publication status: Published}
}

Research article in digital collection (conference)

Hellweg, T., Oehmcke, S., Kariryaa, A., Gieseke, F., & Igel, C. a. F. G. a. C. I. (2022). Ensemble Learning for Semantic Segmentation of Ancient {Maya} Architectures.
More details Full text

2021

Research article in proceedings (conference)

Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K. (2021). Attentional Feature Fusion. In Proceedings of the Workshop on Applications of Computer Vision (WACV), Waikoloa, Hawaii, 3559–3568.
More details BibTeX DOI

@inproceedings{DaiGOWB2021Attentional,
  author = {Yimian Dai and Fabian Gieseke and Stefan Oehmcke and Yiquan Wu and Kobus Barnard},
  title = {Attentional Feature Fusion},
  booktitle = {Proceedings of the Workshop on Applications of Computer Vision (WACV)},
  pages = {3559--3568},
  year = {2021},
  publisher = {IEEE},
  address = {Waikoloa, Hawaii},
  doi = {10.1109/WACV48630.2021.00360},
  note = {Publication status: Published}
}

Munksgaard, P., Breddam, S., Henriksen, T., Gieseke, F., & Oancea, C. E. (2021). Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties — Autotuning in Futhark. In Proceedings of the 22nd International Symposium on Trends in Functional Programming (TFP), Virtual Event, 3–23.
More details BibTeX DOI

@inproceedings{MunksgaardBHGO2021DatasetSensitive,
  author = {P Munksgaard and S Breddam and T Henriksen and F Gieseke and C E Oancea},
  title = {Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties --- Autotuning in Futhark},
  booktitle = {Proceedings of the 22$^{nd}$ International Symposium on Trends in Functional Programming (TFP)},
  pages = {3--23},
  series = {Lecture Notes in Computer Science},
  volume = {12834},
  year = {2021},
  publisher = {Springer},
  address = {Virtual Event},
  doi = {10.1007/978-3-030-83978-9},
  note = {Publication status: Published}
}

Oehmcke, S., Nyegaard-Signori, T., Grogan, K., & Gieseke, F. (2021). Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning. In Chen, Y., Ludwig, H., Tu, Y., Fayyad, U. M., Zhu, X., Hu, X., Byna, S., Liu, X., Zhang, J., Pan, S., Papalexakis, V., Wang, J., Cuzzocrea, A., & Ordonez, C. (Eds.), 2021 {IEEE} International Conference on Big Data (Big Data) (pp. 4915–4924). Orlando, US: Wiley-IEEE Press.
More details BibTeX DOI

@inproceedings{OehmckeNSGG2021Estimating,
  author = {Stefan Oehmcke and Thomas Nyegaard-Signori and Kenneth Grogan and Fabian Gieseke},
  title = {Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning},
  booktitle = {2021 {IEEE} International Conference on Big Data (Big Data)},
  pages = {4915--4924},
  editor = {Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama M. Fayyad and Xingquan Zhu and Xiaohua Hu and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez},
  year = {2021},
  publisher = {Wiley-IEEE Press},
  address = {Orlando, US},
  doi = {10.1109/BigData52589.2021.9672018},
  note = {Publication status: Published}
}

Abstract in Online-Sammlung (Konferenz)

Revenga, J., Trepekli, K., Oehmcke, S., Gieseke, F., Igel, C., Jensen, R., & Friborg, T. (2021). Prediction of above ground biomass and C-stocks based on UAV-LiDAR multispectral imagery and machine learning methods. Poster session presented at the EGU General Assembly 2021, Virtual Event.
More details BibTeX DOI

@misc{RevengaTOGIJF2021Prediction,
  author = {Jaime Revenga and Katerina Trepekli and Stefan Oehmcke and Fabian Gieseke and Christian Igel and Rasmus Jensen and Thomas Friborg},
  title = {Prediction of above ground biomass and C-stocks based on UAV-LiDAR multispectral imagery and machine learning methods},
  year = {2021},
  doi = {10.5194/egusphere-egu21-15708},
  note = {Publication status: Published}
}

Research article (journal)

Masolele, R. N., De, S. V., Herold, M., Marcosa, D., Verbesselt, J., Gieseke, F., Mullissa, A. G., & Martius, C. (2021). Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series. Remote Sensing of Environment, 264, 112600.
More details BibTeX DOI

@article{MasoleleDHMVGMM2021SpatialAnd,
  author = {R N Masolele and Sy V De and M Herold and D Marcosa and J Verbesselt and F Gieseke and A G Mullissa and C Martius},
  title = {Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series},
  journal = {Remote Sensing of Environment},
  year = {2021},
  volume = {264},
  pages = {112600},
  doi = {10.1016/j.rse.2021.112600},
  note = {Publication status: Published}
}

2020

Research article in proceedings (conference)

Dai, Y., Oehmcke, S., Gieseke, F., Wu, Y., & Barnard, K. (2020). Attention as Activation. In Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 9156–9163.
More details BibTeX DOI

@inproceedings{DaiOGWB2020AttentionAs,
  author = {Y Dai and S Oehmcke and F Gieseke and Y Wu and K Barnard},
  title = {Attention as Activation},
  booktitle = {Proceedings of the 25$^{th}$ International Conference on Pattern Recognition (ICPR)},
  pages = {9156--9163},
  year = {2020},
  publisher = {IEEE},
  address = {Milan, Italy},
  doi = {10.1109/ICPR48806.2021.9413020},
  note = {Publication status: Published}
}

Gieseke, F., Rosca, S., Henriksen, T., Verbesselt, J., & Oancea, C. (2020). Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values. In Proceedings of the International Conference on Data Engineering (ICDE), Dallas, USA, 385–396.
More details BibTeX DOI

@inproceedings{GiesekeRHVO2020MassivelyParallel,
  author = {F Gieseke and S Rosca and T Henriksen and J Verbesselt and C Oancea},
  title = {Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values},
  booktitle = {Proceedings of the 36$^{th}$ IEEE International Conference on Data Engineering (ICDE)},
  pages = {385--396},
  year = {2020},
  address = {Dallas, USA},
  doi = {10.1109/ICDE48307.2020.00040},
  note = {Publication status: Published}
}

Oancea, C. E., Robroek, T., & Gieseke, F. (2020). Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees. In Wu, X., Jermaine, C., Xiong, L., Hu, X., Kotevska, O., Lu, S., Xu, W., Aluru, S., Zhai, C., Eyhab, A.-}. C., Chen, Z., & Saltz, J. (Eds.), 2020 {IEEE} International Conference on Big Data {(IEEE} BigData 2020 (pp. 5172–5181). Atlanta, GA, USA: IEEE.
More details BibTeX DOI

@inproceedings{OanceaRG2020Approximate,
  author = {Cosmin Eugen Oancea and Ties Robroek and Fabian Gieseke},
  title = {Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees},
  booktitle = {2020 {IEEE} International Conference on Big Data {(IEEE} BigData 2020},
  pages = {5172--5181},
  editor = {Xintao Wu and Chris Jermaine and Li Xiong and Xiaohua Hu and Olivera Kotevska and Siyuan Lu and Weija Xu and Srinivas Aluru and Chengxiang Zhai and Al{-}Masri Chen Eyhab and Zhiyuan Chen and Jeff Saltz},
  year = {2020},
  publisher = {IEEE},
  address = {Atlanta, GA, USA},
  doi = {10.1109/BigData50022.2020.9378426},
  note = {Publication status: Published}
}

Oehmcke, S., Tzu-Hsin, K. C., Prishchepov, A. V., & Gieseke, F. (2020). Creating Cloud-Free Satellite Imagery from Image Time Series with Deep Learning. In Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial 2020), Seattle, USA, 3:1-3:10.
More details BibTeX DOI

@inproceedings{OehmckeTHPG2020CreatingCloudFree,
  author = {S Oehmcke and K C Tzu-Hsin and A V Prishchepov and F Gieseke},
  title = {Creating Cloud-Free Satellite Imagery from Image Time Series with Deep Learning},
  booktitle = {Proceedings of the 9$^{th}$ ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial 2020)},
  pages = {3:1-3:10},
  year = {2020},
  publisher = {ACM},
  address = {Seattle, USA},
  doi = {10.1145/3423336.3429345},
  note = {Publication status: Published}
}

Research article (journal)

Brandt, M., Tucker, C., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L., Hiernaux, P., Diouf, A., Kergoat, L., Mertz, O., Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J., SinnoS, , Romero, E., Glennie, E., Montagu, A., Dendoncker, M., & Fensholt, R. (2020). An unexpectedly large count of trees in the West African Sahara and Sahel. Nature, 2020.
More details BibTeX DOI

@article{BrandtTKRASCRHDKMIGSLMMSRGMDF2020AnUnexpectedly,
  author = {M Brandt and C Tucker and A Kariryaa and K Rasmussen and C Abel and J Small and J Chave and L Rasmussen and P Hiernaux and A Diouf and L Kergoat and O Mertz and C Igel and F Gieseke and J Schöning and S Li and K Melocik and J Meyer and  SinnoS and E Romero and E Glennie and A Montagu and M Dendoncker and R Fensholt},
  title = {An unexpectedly large count of trees in the West African Sahara and Sahel},
  journal = {Nature},
  year = {2020},
  volume = {2020},
  doi = {10.1038/s41586-020-2824-5},
  note = {Publication status: Published}
}

Hamunyela, E., Rosca, S., Mirt, A., Engle, E., Herold, M., Gieseke, F., & Verbesselt, J. (2020). Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data. Remote Sensing (Remote Sens.), 12(18).
More details BibTeX DOI

@article{HamunyelaRMEHGV2020Implementation,
  author = {E Hamunyela and S Rosca and A Mirt and E Engle and M Herold and F Gieseke and J Verbesselt},
  title = {Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data},
  journal = {Remote Sensing (Remote Sens.)},
  year = {2020},
  volume = {12},
  number = {18},
  doi = {10.3390/rs12182953},
  note = {Publication status: Published}
}

2019

Research article in proceedings (conference)

Ko, V., Oehmcke, S., & Gieseke, F. (2019). Magnitude and Uncertainty Pruning Criterion for Neural Networks. In Baru, C. K., Huan, J., Khan, L., Hu, X., Ak, R., Tian, Y., Barga, R. S., Zaniolo, C., Lee, K., & Ye, Y. (. (Eds.), 2019 {IEEE} International Conference on Big Data {(IEEE} BigData) (pp. 2317–2326). Los Angeles, USA: IEEE.
More details BibTeX Full text DOI

@inproceedings{KoOG2019MagnitudeAnd,
  author = {Vinnie Ko and Stefan Oehmcke and Fabian Gieseke},
  title = {Magnitude and Uncertainty Pruning Criterion for Neural Networks},
  booktitle = {2019 {IEEE} International Conference on Big Data {(IEEE} BigData)},
  pages = {2317--2326},
  editor = {Chaitanya K. Baru and Jun Huan and Latifur Khan and Xiaohua Hu and Ronay Ak and Yuanyuan Tian and Roger S. Barga and Carlo Zaniolo and Kisung Lee and Yanfang (Fanny) Ye},
  year = {2019},
  publisher = {IEEE},
  address = {Los Angeles, USA},
  doi = {10.1109/BigData47090.2019.9005692},
  url = {https://doi.org/10.1109/BigData47090.2019.9005692},
  note = {Publication status: Published}
}

Oehmcke, S., Thrysøe, C., Borgstad, A. S., Marcos, V., Brandt, M., & Gieseke, F. (2019). Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. In Baru, C. K., Huan, J., Khan, L., Hu, X., Ak, R., Tian, Y., Barga, R. S., Zaniolo, C., Lee, K., & Ye, Y. (. (Eds.), 2019 {IEEE} International Conference on Big Data {(IEEE} BigData) (pp. 2403–2412). Los Angeles, USA: IEEE.
More details BibTeX DOI

@inproceedings{OehmckeTBMBG2019DetectingHardly,
  author = {Stefan Oehmcke and Christoph Thrysøe and Andreas: Salles Borgstad and Vaz Marcos and Martin Brandt and Fabian Gieseke},
  title = {Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data},
  booktitle = {2019 {IEEE} International Conference on Big Data {(IEEE} BigData)},
  pages = {2403--2412},
  editor = {Chaitanya K. Baru and Jun Huan and Latifur Khan and Xiaohua Hu and Ronay Ak and Yuanyuan Tian and Roger S. Barga and Carlo Zaniolo and Kisung Lee and Yanfang (Fanny) Ye},
  year = {2019},
  publisher = {IEEE},
  address = {Los Angeles, USA},
  doi = {10.1109/BigData47090.2019.9006251},
  note = {Publication status: Published}
}

2018

Research article in proceedings (conference)

Gieseke, F., & Igel, C. (2018). Training Big Random Forests with Little Resources. In Guo, Y., & Farooq, F. (Eds.), Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018 (pp. 1445–1454). ACM.
More details BibTeX Full text DOI

@inproceedings{GiesekeI2018TrainingBig,
  author = {F Gieseke and C Igel},
  title = {Training Big Random Forests with Little Resources},
  booktitle = {Proceedings of the 24$^{th}$ ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018},
  pages = {1445--1454},
  editor = {Y Guo and F Farooq},
  year = {2018},
  publisher = {ACM},
  address = {London, GB},
  doi = {10.1145/3219819.3220124},
  url = {https://doi.org/10.1145/3219819.3220124},
  note = {Publication status: Published}
}

Gieseke, F., Oancea, C., Mahabal, A., Igel, C., & Heskes, T. (2018). Bigger Buffer k-d Trees on Multi-Many-Core Systems. In Senger, H., Marques, O., Garcia, R., Brito, T., Iope, R., Stanzani, S., & Costa, V. (Eds.), High Performance Computing for Computational Science — VECPAR 2018 — 13th International Conference, São Pedro, Brazil, September 17-19, 2018, Revised Selected Papers (pp. 202–214). Lecture Notes in Computer Science: Vol. 11333. Springer.
More details BibTeX Full text DOI

@inproceedings{GiesekeOMIH2018BiggerBuffer,
  author = {F Gieseke and CE Oancea and A Mahabal and C Igel and T Heskes},
  title = {Bigger Buffer k-d Trees on Multi-Many-Core Systems},
  booktitle = {High Performance Computing for Computational Science --- VECPAR 2018 --- 13$^{th}$ International Conference, São Pedro, Brazil, September 17-19, 2018, Revised Selected Papers},
  pages = {202--214},
  series = {Lecture Notes in Computer Science},
  volume = {11333},
  editor = {H Senger and O Marques and RE Garcia and TP Brito and R Iope and SL Stanzani and VG Costa},
  year = {2018},
  publisher = {Springer},
  address = {Brazil, September},
  doi = {10.1007/978-3-030-15996-2\_15},
  url = {https://doi.org/10.1007/978-3-030-15996-2\_15},
  note = {Publication status: Published}
}

Mehren, M., Gieseke, F., Verbesselt, J., Rosca, S., Horion, S., & Zeileis, A. (2018). Massively-parallel break detection for satellite data. In Sacharidis, D., Gamper, J., & Böhlen, M. (Eds.), Proceedings of the 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018, Bozen-Bolzano, Italy, July 09-11, 2018 (pp. 5:1-5:10). ACM.
More details BibTeX Full text DOI

@inproceedings{MehrenGVRHZ2018MassivelyParallel,
  author = {M Mehren and F Gieseke and J Verbesselt and S Rosca and S Horion and A Zeileis},
  title = {Massively-parallel break detection for satellite data},
  booktitle = {Proceedings of the 30$^{th}$ International Conference on Scientific and Statistical Database Management, SSDBM 2018, Bozen-Bolzano, Italy, July 09-11, 2018},
  pages = {5:1-5:10},
  editor = {D Sacharidis and J Gamper and MH Böhlen},
  year = {2018},
  publisher = {ACM},
  address = {Bozen-Bolzano, Italy},
  doi = {10.1145/3221269.3223032},
  url = {https://doi.org/10.1145/3221269.3223032},
  note = {Publication status: Published}
}

Research article (journal)

D'Isanto, A., Cavuoti, S., Gieseke, F., & Polsterer, K. (2018). Return of the features — Efficient feature selection and interpretation for photometric redshifts. Astronomy & Astrophysics, 616, A97.
More details BibTeX DOI

@article{DIsantoCGP2018ReturnOfThe,
  author = {A D'Isanto and S Cavuoti and F Gieseke and KL Polsterer},
  title = {Return of the features --- Efficient feature selection and interpretation for photometric redshifts},
  journal = {Astronomy & Astrophysics},
  year = {2018},
  volume = {616},
  pages = {A97},
  doi = {10.1051/0004-6361/201833103},
  note = {Publication status: Published}
}

Florea, C., & Gieseke, F. (2018). Artistic movement recognition by consensus of boosted SVM based experts. Journal of Visual Communication and Image Representation, 56, 220–233.
More details BibTeX Full text DOI

@article{FloreaG2018ArtisticMovement,
  author = {C Florea and F Gieseke},
  title = {Artistic movement recognition by consensus of boosted SVM based experts},
  journal = {Journal of Visual Communication and Image Representation},
  year = {2018},
  volume = {56},
  pages = {220--233},
  doi = {10.1016/j.jvcir.2018.09.015},
  url = {https://doi.org/10.1016/j.jvcir.2018.09.015},
  note = {Publication status: Published}
}

2017

Research article in proceedings (conference)

Florea, C., Toca, C., & Gieseke, F. (2017). Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, CA, USA, 569–577.
More details BibTeX Full text DOI

@inproceedings{FloreaTG2017ArtisticMovement,
  author = {C Florea and C Toca and F Gieseke},
  title = {Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description},
  booktitle = {2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, Santa Rosa, CA, USA, March 24-31, 2017},
  pages = {569--577},
  year = {2017},
  publisher = {IEEE Computer Society},
  address = {Santa Rosa, CA, USA},
  doi = {10.1109/WACV.2017.69},
  url = {https://doi.org/10.1109/WACV.2017.69},
  note = {Publication status: Published}
}

Gieseke, F., Polsterer, K., Mahabal, A., Igel, C., & Heskes, T. (2017). Massively-parallel best subset selection for ordinary least-squares regression. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, HI, USA, 1–8.
More details BibTeX Full text DOI

@inproceedings{GiesekePMIH2017MassivelyParallel,
  author = {F Gieseke and KL Polsterer and A Mahabal and C Igel and T Heskes},
  title = {Massively-parallel best subset selection for ordinary least-squares regression},
  booktitle = {2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, HI, USA, November 27 --- Dec. 1, 2017},
  pages = {1--8},
  year = {2017},
  publisher = {IEEE},
  address = {Honolulu, HI, USA},
  doi = {10.1109/SSCI.2017.8285225},
  url = {https://doi.org/10.1109/SSCI.2017.8285225},
  note = {Publication status: Published}
}

Mahabal, A., Sheth, K., Gieseke, F., Pai, A., Djorgovski, S., Drake, A., & Graham, M. (2017). Deep-learnt classification of light curves. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, HI, USA, 1–8.
More details BibTeX Full text DOI

@inproceedings{MahabalSGPDDG2017DeepLearnt,
  author = {A Mahabal and K Sheth and F Gieseke and A Pai and SG Djorgovski and AJ Drake and MJ Graham},
  title = {Deep-learnt classification of light curves},
  booktitle = {2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, HI, USA, November 27 --- Dec. 1, 2017},
  pages = {1--8},
  year = {2017},
  publisher = {IEEE},
  address = {Honolulu, HI, USA},
  doi = {10.1109/SSCI.2017.8280984},
  url = {https://doi.org/10.1109/SSCI.2017.8280984},
  note = {Publication status: Published}
}

Research article (journal)

Beck, R., Lin, C., Ishida, E., Gieseke, F., Souza, R., Costa-Duarte, M., Hattab, M., & Krone-Martins, A. (2017). On the realistic validation of photometric redshifts. Monthly Notices of the Royal Astronomical Society (MNRAS), 468(4), 4323–4339.
More details BibTeX DOI

@article{BeckLIGSCDHKM2017OnTheRealistic,
  author = {R Beck and CA Lin and EO Ishida and F Gieseke and RS Souza and MV Costa-Duarte and MW Hattab and A Krone-Martins},
  title = {On the realistic validation of photometric redshifts},
  journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
  year = {2017},
  volume = {468},
  number = {4},
  pages = {4323--4339},
  doi = {10.1093/mnras/stx687},
  note = {Publication status: Published}
}

Gieseke, F., Bloemen, S., Bogaard, C., Heskes, T., Kindler, J., Scalzo, R., Ribeiro, V., Roestel, J., Groot, P., Yuan, F., Möller, A., & Tucker, B. (2017). Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys. Monthly Notices of the Royal Astronomical Society (MNRAS), 472(3), 3101–3114.
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@article{GiesekeBBHKSRRGYMT2017Convolutional,
  author = {F Gieseke and S Bloemen and C Bogaard and T Heskes and J Kindler and RA Scalzo and VA Ribeiro and J Roestel and PJ Groot and F Yuan and A Möller and BE Tucker},
  title = {Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys},
  journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
  year = {2017},
  volume = {472},
  number = {3},
  pages = {3101--3114},
  doi = {10.1093/mnras/stx2161},
  note = {Publication status: Published}
}

Gieseke, F., Oancea, C., & Igel, C. (2017). bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge Based Systems, 120, 1–3.
More details BibTeX Full text DOI

@article{GiesekeOI2017Bufferkdtree,
  author = {F Gieseke and CE Oancea and C Igel},
  title = {bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices},
  journal = {Knowledge Based Systems},
  year = {2017},
  volume = {120},
  pages = {1--3},
  doi = {10.1016/j.knosys.2017.01.002},
  url = {https://doi.org/10.1016/j.knosys.2017.01.002},
  note = {Publication status: Published}
}

Kremer, J., Stensbo-Smidt, K., Gieseke, F., Pedersen, K., & Igel, C. (2017). Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intelligent Systems, 32(2), 16–22.
More details BibTeX Full text DOI

@article{KremerSSGPI2017BigUniverse,
  author = {J Kremer and K Stensbo-Smidt and F Gieseke and KS Pedersen and C Igel},
  title = {Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy},
  journal = {IEEE Intelligent Systems},
  year = {2017},
  volume = {32},
  number = {2},
  pages = {16--22},
  doi = {10.1109/MIS.2017.40},
  url = {https://doi.org/10.1109/MIS.2017.40},
  note = {Publication status: Published}
}

Souza, R., Dantas, M., Costa-Duarte, M., Feigelson, E., Killedar, M., Lablanche, P., Vilalta, R., Krone-Martins, A., Beck, R., & Gieseke, F. (2017). A probabilistic approach to emission-line galaxy classification. Monthly Notices of the Royal Astronomical Society (MNRAS), 472(3).
More details BibTeX DOI

@article{SouzaDCDFKLVKMBG2017AProbabilistic,
  author = {RS Souza and MLL Dantas and MV Costa-Duarte and ED Feigelson and M Killedar and P Lablanche and R Vilalta and A Krone-Martins and R Beck and F Gieseke},
  title = {A probabilistic approach to emission-line galaxy classification},
  journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
  year = {2017},
  volume = {472},
  number = {3},
  doi = {10.1093/mnras/stx2156},
  note = {Publication status: Published}
}

Stensbo-Smidt, K., Gieseke, F., Zirm, A., Pedersen, K., & Igel, C. (2017). Sacrificing information for the greater good: how to select photometric bands for optimal accuracy. Monthly Notices of the Royal Astronomical Society (MNRAS), 464(3), 2577–2596.
More details BibTeX DOI

@article{StensboSmidtGZPI2017Sacrificing,
  author = {K Stensbo-Smidt and F Gieseke and A Zirm and KS Pedersen and C Igel},
  title = {Sacrificing information for the greater good: how to select photometric bands for optimal accuracy},
  journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
  year = {2017},
  volume = {464},
  number = {3},
  pages = {2577--2596},
  doi = {10.1093/mnras/stw2476},
  note = {Publication status: Published}
}

2016

Research article in proceedings (conference)

Ishida, E., Sasdelli, M., Vilalta, R., Aguena, M., Busti, V., Camacho, H., Trindade, A., Gieseke, F., Souza, R., Fantaye, Y., & Mazzali, P. (2016). Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. In Brescia, M., Djorgovski, S., Feigelson, E., Longo, G., & Cavuoti, S. (Eds.), Proceedings of the International Astronomical Union (pp. 247–252). Proceedings of the International Astronomical Union: Vol. 12. Cambridge University Press.
More details BibTeX Full text DOI

@inproceedings{IshidaSVABCTGSFM2016ExploringThe,
  author = {EEO Ishida and M Sasdelli and R Vilalta and M Aguena and VC Busti and H Camacho and AMM Trindade and F Gieseke and RS Souza and YT Fantaye and PA Mazzali},
  title = {Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering},
  booktitle = {Proceedings of the International Astronomical Union},
  pages = {247--252},
  series = {Proceedings of the International Astronomical Union},
  volume = {12},
  editor = {M Brescia and SG Djorgovski and ED Feigelson and G Longo and S Cavuoti},
  year = {2016},
  publisher = {Cambridge University Press},
  address = {Sorrento, Italy},
  doi = {10.1017/S174392131601293X},
  url = {https://doi.org/10.1017/S174392131601293X},
  note = {Publication status: Published}
}

Polsterer, K., Gieseke, F., Igel, C., Doser, B., & Gianniotis, N. (2016). Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs. In Proceedings of the 24th European Symposium on Artificial Neural Networks, Bruges, Belgium.
More details BibTeX Full text

@inproceedings{PolstererGIDG2016Parallelized,
  author = {KL Polsterer and F Gieseke and C Igel and B Doser and N Gianniotis},
  title = {Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs},
  booktitle = {24$^{th}$ European Symposium on Artificial Neural Networks, ESANN 2016, Bruges, Belgium, April 27-29, 2016},
  year = {2016},
  address = {Bruges, Belgium},
  url = {http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-116.pdf},
  note = {Publication status: Published}
}

Research article (journal)

Sasdelli, M., Ishida, E., Vilalta, R., Aguena, M., Busti, V., Camacho, H., Trindade, A., Gieseke, F., Souza, R., Fantaye, Y., & Mazzali, P. (2016). Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: A machine learning approach. Monthly Notices of the Royal Astronomical Society (MNRAS), 461(2), 2044–2059.
More details BibTeX DOI

@article{SasdelliIVABCTGSFM2016ExploringThe,
  author = {M Sasdelli and EO Ishida and R Vilalta and M Aguena and VC Busti and H Camacho and AMM Trindade and F Gieseke and RS Souza and YT Fantaye and PA Mazzali},
  title = {Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: A machine learning approach},
  journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
  year = {2016},
  volume = {461},
  number = {2},
  pages = {2044--2059},
  doi = {10.1093/mnras/stw1228},
  note = {Publication status: Published}
}

2015

Research article in proceedings (conference)

Gieseke, F. (2015). An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines. In Pardalos, P., Pavone, M., Farinella, G., & Cutello, V. (Eds.), Machine Learning, Optimization, and Big Data — First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers (pp. 145–157). Lecture Notes in Computer Science: Vol. 9432. Springer.
More details BibTeX Full text DOI

@inproceedings{Gieseke2015AnEfficient,
  author = {F Gieseke},
  title = {An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines},
  booktitle = {Machine Learning, Optimization, and Big Data --- First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers},
  pages = {145--157},
  series = {Lecture Notes in Computer Science},
  volume = {9432},
  editor = {PM Pardalos and M Pavone and GM Farinella and V Cutello},
  year = {2015},
  publisher = {Springer},
  address = {Taormina, Sicily, Italy},
  doi = {10.1007/978-3-319-27926-8\_13},
  url = {https://doi.org/10.1007/978-3-319-27926-8\_13},
  note = {Publication status: Published}
}

Gieseke, F., Pahikkala, T., & Heskes, T. (2015). Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. In Fromont, B. T., & Leeuwen, M. (Eds.), Advances in Intelligent Data Analysis XIV — 14th International Symposium, IDA 2015, Saint Etienne, France, October 22-24, 2015, Proceedings (pp. 95–107). Lecture Notes in Computer Science: Vol. 9385. Springer.
More details BibTeX Full text DOI

@inproceedings{GiesekePH2015BatchSteepestDescentMildestAscent,
  author = {F Gieseke and T Pahikkala and T Heskes},
  title = {Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering},
  booktitle = {Advances in Intelligent Data Analysis XIV --- 14$^{th}$ International Symposium, IDA 2015, Saint Etienne, France, October 22-24, 2015, Proceedings},
  pages = {95--107},
  series = {Lecture Notes in Computer Science},
  volume = {9385},
  editor = {Bie TD Fromont and M Leeuwen},
  year = {2015},
  publisher = {Springer},
  address = {Saint Etienne, France},
  doi = {10.1007/978-3-319-24465-5\_9},
  url = {https://doi.org/10.1007/978-3-319-24465-5\_9},
  note = {Publication status: Published}
}

Research article (journal)

Kremer, J., Gieseke, F., Pedersen, K., & Igel, C. (2015). Nearest Neighbor Density Ratio Estimation for Large-Scale Applications in Astronomy. Astronomy and Computing, 12, 62–72.
More details BibTeX DOI

@article{KremerGPI2015NearestNeighbor,
  author = {J Kremer and F Gieseke and KS Pedersen and C Igel},
  title = {Nearest Neighbor Density Ratio Estimation for Large-Scale Applications in Astronomy},
  journal = {Astronomy and Computing},
  year = {2015},
  volume = {12},
  pages = {62--72},
  doi = {10.1016/j.ascom.2015.06.005},
  note = {Publication status: Published}
}

2014

Research article in proceedings (conference)

Gieseke, F., Heinermann, J., Oancea, C., & Igel, C. (2014). Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. In Proceedings of the 31th International Conference on Machine Learning, Beijing, China, 172–180.
More details BibTeX Full text

@inproceedings{GiesekeHOI2014BufferKDTrees,
  author = {F Gieseke and J Heinermann and CE Oancea and C Igel},
  title = {Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs},
  booktitle = {Proceedings of the 31$^{th}$ International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014},
  pages = {172--180},
  series = {JMLR Workshop and Conference Proceedings},
  volume = {32},
  year = {2014},
  publisher = {JMLR.org},
  address = {Beijing, China},
  url = {http://proceedings.mlr.press/v32/gieseke14.html},
  note = {Publication status: Published}
}

Gieseke, F., Polsterer, K., Oancea, C., & Igel, C. (2014). Speedy greedy feature selection: Better redshift estimation via massive parallelism. In Proceedings of the 22th European Symposium on Artificial Neural Networks, Bruges, Belgium.
More details BibTeX Full text

@inproceedings{GiesekePOI2014SpeedyGreedy,
  author = {F Gieseke and KL Polsterer and CE Oancea and C Igel},
  title = {Speedy greedy feature selection: Better redshift estimation via massive parallelism},
  booktitle = {22$^{th}$ European Symposium on Artificial Neural Networks, ESANN 2014, Bruges, Belgium, April 23-25, 2014},
  year = {2014},
  address = {Bruges, Belgium},
  url = {http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-171.pdf},
  note = {Publication status: Published}
}

Kramer, O., Gieseke, F., Heinermann, J., Poloczek, J., & Treiber, N. (2014). A Framework for Data Mining in Wind Power Time Series. In Woon, W., Aung, Z., & Madnick, S. (Eds.), Data Analytics for Renewable Energy Integration — Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers (pp. 97–107). Lecture Notes in Computer Science: Vol. 8817. Springer.
More details BibTeX Full text DOI

@inproceedings{KramerGHPT2014AFramework,
  author = {O Kramer and F Gieseke and J Heinermann and J Poloczek and NA Treiber},
  title = {A Framework for Data Mining in Wind Power Time Series},
  booktitle = {Data Analytics for Renewable Energy Integration --- Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers},
  pages = {97--107},
  series = {Lecture Notes in Computer Science},
  volume = {8817},
  editor = {WL Woon and Z Aung and SE Madnick},
  year = {2014},
  publisher = {Springer},
  address = {Nancy, France},
  doi = {10.1007/978-3-319-13290-7\_8},
  url = {https://doi.org/10.1007/978-3-319-13290-7\_8},
  note = {Publication status: Published}
}

Research article (journal)

Gieseke, F., Airola, A., Pahikkala, T., & Kramer, O. (2014). Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing, 123, 23–32.
More details BibTeX Full text DOI

@article{GiesekeAPK2014FastAndSimple,
  author = {F Gieseke and A Airola and T Pahikkala and O Kramer},
  title = {Fast and simple gradient-based optimization for semi-supervised support vector machines},
  journal = {Neurocomputing},
  year = {2014},
  volume = {123},
  pages = {23--32},
  doi = {10.1016/j.neucom.2012.12.056},
  url = {https://doi.org/10.1016/j.neucom.2012.12.056},
  note = {Publication status: Published}
}

Pahikkala, T., Airola, A., Gieseke, F., & Kramer, O. (2014). On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers. Journal of Computer Science and Technology (ICDM 2012 Special Issue), 29(1), 90–104.
More details BibTeX Full text DOI

@article{PahikkalaAGK2014OnUnsupervised,
  author = {T Pahikkala and A Airola and F Gieseke and O Kramer},
  title = {On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers},
  journal = {Journal of Computer Science and Technology (ICDM 2012 Special Issue)},
  year = {2014},
  volume = {29},
  number = {1},
  pages = {90--104},
  doi = {10.1007/s11390-014-1414-0},
  url = {https://doi.org/10.1007/s11390-014-1414-0},
  note = {Publication status: Published}
}

2013

Research article in proceedings (conference)

Gieseke, F., & Kramer, O. (2013). Towards Non-linear Constraint Estimation for Expensive Optimization. In Esparcia{-}Alc{á}zar, A. (Ed.), Applications of Evolutionary Computation — 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings (pp. 459–468). Lecture Notes in Computer Science: Vol. 7835. Springer.
More details BibTeX Full text DOI

@inproceedings{GiesekeK2013TowardsNonLinear,
  author = {F Gieseke and O Kramer},
  title = {Towards Non-linear Constraint Estimation for Expensive Optimization},
  booktitle = {Applications of Evolutionary Computation --- 16$^{th}$ European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings},
  pages = {459--468},
  series = {Lecture Notes in Computer Science},
  volume = {7835},
  editor = {AI Esparcia{-}Alc{á}zar},
  year = {2013},
  publisher = {Springer},
  address = {Vienna, Austria},
  doi = {10.1007/978-3-642-37192-9\_46},
  url = {https://doi.org/10.1007/978-3-642-37192-9\_46},
  note = {Publication status: Published}
}

Gieseke, F., Pahikkala, T., & Igel, C. (2013). Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Ong, C., & Ho, T. (Eds.), Asian Conference on Machine Learning, ACML 2013, Canberra, ACT, Australia, November 13-15, 2013 (pp. 62–71). JMLR Workshop and Conference Proceedings: Vol. 29. JMLR.org.
More details BibTeX Full text

@inproceedings{GiesekePI2013Polynomial,
  author = {F Gieseke and T Pahikkala and C Igel},
  title = {Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification},
  booktitle = {Asian Conference on Machine Learning, ACML 2013, Canberra, ACT, Australia, November 13-15, 2013},
  pages = {62--71},
  series = {JMLR Workshop and Conference Proceedings},
  volume = {29},
  editor = {CS Ong and TB Ho},
  year = {2013},
  publisher = {JMLR.org},
  address = {Canberra, ACT, Australia},
  url = {http://proceedings.mlr.press/v29/Gieseke13.html},
  note = {Publication status: Published}
}

Heinermann, J., Kramer, O., Polsterer, K., & Gieseke, F. (2013). On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. In Timm, I., & Thimm, M. (Eds.), KI 2013: Advances in Artificial Intelligence — 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013. Proceedings (pp. 86–97). Lecture Notes in Computer Science: Vol. 8077. Springer.
More details BibTeX Full text DOI

@inproceedings{HeinermannKPG2013OnGPUBased,
  author = {J Heinermann and O Kramer and KL Polsterer and F Gieseke},
  title = {On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy},
  booktitle = {KI 2013: Advances in Artificial Intelligence --- 36$^{th}$ Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013. Proceedings},
  pages = {86--97},
  series = {Lecture Notes in Computer Science},
  volume = {8077},
  editor = {IJ Timm and M Thimm},
  year = {2013},
  publisher = {Springer},
  address = {Koblenz, Germany},
  doi = {10.1007/978-3-642-40942-4\_8},
  url = {https://doi.org/10.1007/978-3-642-40942-4\_8},
  note = {Publication status: Published}
}

Kramer, O., Treiber, N., & Gieseke, F. (2013). Support Vector Machines for Wind Energy Prediction in Smart Grids. In Page, B., Fleischer, A., Göbel, J., & Wohlgemuth, V. (Eds.), 27th International Conference on Environmental Informatics for Environmental Protection, Sustainable Development and Risk Management, EnviroInfo 2013, Hamburg, Germany, September 2-4, 2013. Proceedings (pp. 16–24). Shaker.
More details BibTeX Full text

@inproceedings{KramerTG2013SupportVector,
  author = {O Kramer and NA Treiber and F Gieseke},
  title = {Support Vector Machines for Wind Energy Prediction in Smart Grids},
  booktitle = {27$^{th}$ International Conference on Environmental Informatics for Environmental Protection, Sustainable Development and Risk Management, EnviroInfo 2013, Hamburg, Germany, September 2-4, 2013. Proceedings},
  pages = {16--24},
  editor = {B Page and AG Fleischer and J Göbel and V Wohlgemuth},
  year = {2013},
  publisher = {Shaker},
  address = {Hamburg, Germany},
  url = {http://iai-uiserv1.iai.fzk.de/ictensure/site?mod=litdb&\#38;subject=art&\#38;pid=XE6C64FA5&\#38;action=detail},
  note = {Publication status: Published}
}

Research article (journal)

Gieseke, F. (2013). From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. KI, 27(3), 281–285.
More details BibTeX Full text DOI

@article{Gieseke2013FromSupervised,
  author = {F Gieseke},
  title = {From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy},
  journal = {KI},
  year = {2013},
  volume = {27},
  number = {3},
  pages = {281--285},
  doi = {10.1007/s13218-013-0248-1},
  url = {https://doi.org/10.1007/s13218-013-0248-1},
  note = {Publication status: Published}
}

Gieseke, F. (2013). Von überwachten zu unüberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie. Ausgezeichnete Informatikdissertationen 2012, D-13, 111–120.
More details BibTeX

@article{Gieseke2013VonUeberwachten,
  author = {F Gieseke},
  title = {Von überwachten zu unüberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie},
  journal = {Ausgezeichnete Informatikdissertationen 2012},
  year = {2013},
  volume = {D-13},
  pages = {111--120},
  note = {Publication status: Published}
}

Kramer, O., Gieseke, F., & Polsterer, K. (2013). Learning morphological maps of galaxies with unsupervised regression. Expert Systems with Applications, 40(8), 2841–2844.
More details BibTeX Full text DOI

@article{KramerGP2013LearningMorphological,
  author = {O Kramer and F Gieseke and KL Polsterer},
  title = {Learning morphological maps of galaxies with unsupervised regression},
  journal = {Expert Systems with Applications},
  year = {2013},
  volume = {40},
  number = {8},
  pages = {2841--2844},
  doi = {10.1016/j.eswa.2012.12.002},
  url = {https://doi.org/10.1016/j.eswa.2012.12.002},
  note = {Publication status: Published}
}

Kramer, O., Gieseke, F., & Satzger, B. (2013). Wind energy prediction and monitoring with neural computation. Neurocomputing, 109, 84–93.
More details BibTeX Full text DOI

@article{KramerGS2013WindEnergy,
  author = {O Kramer and F Gieseke and B Satzger},
  title = {Wind energy prediction and monitoring with neural computation},
  journal = {Neurocomputing},
  year = {2013},
  volume = {109},
  pages = {84--93},
  doi = {10.1016/j.neucom.2012.07.029},
  url = {https://doi.org/10.1016/j.neucom.2012.07.029},
  note = {Publication status: Published}
}

Polsterer, K., Zinn, P., & Gieseke, F. (2013). Finding New High-Redshift Quasars by Asking the Neighbours. Monthly Notices of the Royal Astronomical Society (MNRAS), 428(1), 226–235.
More details BibTeX DOI

@article{PolstererZG2013FindingNew,
  author = {KL Polsterer and P Zinn and F Gieseke},
  title = {Finding New High-Redshift Quasars by Asking the Neighbours},
  journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
  year = {2013},
  volume = {428},
  number = {1},
  pages = {226--235},
  doi = {10.1093/mnras/sts017},
  note = {Publication status: Published}
}

2012

Research article in proceedings (conference)

Gieseke, F., Airola, A., Pahikkala, T., & Kramer, O. (2012). Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines. In Carmona, P., Sánchez, J., & Fred, A. (Eds.), ICPRAM 2012 — Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, Volume 1, Vilamoura, Algarve, Portugal, 6-8 February, 2012 (pp. 45–54). SciTePress.
More details BibTeX

@inproceedings{GiesekeAPK2012SparseQuasiNewton,
  author = {F Gieseke and A Airola and T Pahikkala and O Kramer},
  title = {Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines},
  booktitle = {ICPRAM 2012 --- Proceedings of the 1$^{st}$ International Conference on Pattern Recognition Applications and Methods, Volume 1, Vilamoura, Algarve, Portugal, 6-8 February, 2012},
  pages = {45--54},
  editor = {PL Carmona and JS Sánchez and ALN Fred},
  year = {2012},
  publisher = {SciTePress},
  address = {Vilamoura, Algarve, Portugal},
  note = {Publication status: Published}
}

Pahikkala, T., Airola, A., Gieseke, F., & Kramer, O. (2012). Unsupervised Multi-class Regularized Least-Squares Classification. In Zaki, M., Siebes, A., Yu, J., Goethals, B., Webb, G., & Wu, X. (Eds.), 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012 (pp. 585–594). IEEE Computer Society.
More details BibTeX Full text DOI

@inproceedings{PahikkalaAGK2012Unsupervised,
  author = {T Pahikkala and A Airola and F Gieseke and O Kramer},
  title = {Unsupervised Multi-class Regularized Least-Squares Classification},
  booktitle = {12$^{th}$ IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012},
  pages = {585--594},
  editor = {MJ Zaki and A Siebes and JX Yu and B Goethals and GI Webb and X Wu},
  year = {2012},
  publisher = {IEEE Computer Society},
  address = {Brussels, Belgium},
  doi = {10.1109/ICDM.2012.71},
  url = {https://doi.org/10.1109/ICDM.2012.71},
  note = {Publication status: Published}
}

Research article (journal)

Gieseke, F., Kramer, O., Airola, A., & Pahikkala, T. (2012). Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification. Evolutionary Intelligence, 5(3), 189–205.
More details BibTeX Full text DOI

@article{GiesekeKAP2012EfficientRecurrent,
  author = {F Gieseke and O Kramer and A Airola and T Pahikkala},
  title = {Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification},
  journal = {Evolutionary Intelligence},
  year = {2012},
  volume = {5},
  number = {3},
  pages = {189--205},
  doi = {10.1007/s12065-012-0068-5},
  url = {https://doi.org/10.1007/s12065-012-0068-5},
  note = {Publication status: Published}
}

Gieseke, F., Moruz, G., & Vahrenhold, J. (2012). Resilient k-d trees: k-means in space revisited. Frontiers of Computer Science (ICDM 2010 Special Issue), 6(2), 166–178.
More details BibTeX Full text DOI

@article{GiesekeMV2012ResilientKD,
  author = {F Gieseke and G Moruz and J Vahrenhold},
  title = {Resilient k-d trees: k-means in space revisited},
  journal = {Frontiers of Computer Science (ICDM 2010 Special Issue)},
  year = {2012},
  volume = {6},
  number = {2},
  pages = {166--178},
  doi = {10.1007/s11704-012-2870-8},
  url = {https://doi.org/10.1007/s11704-012-2870-8},
  note = {Publication status: Published}
}

Gieseke, F., Moruz, G., & Vahrenhold, J. (2012). Resilient K-d Trees: K-Means in Space Revisited. Frontiers of Computer Science, 6(2), 166–178.
More details BibTeX DOI

@article{GiesekeMV2012ResilientKD,
  author = {Fabian Gieseke and Gabriel Moruz and Jan Vahrenhold},
  title = {Resilient K-d Trees: K-Means in Space Revisited},
  journal = {Frontiers of Computer Science},
  year = {2012},
  volume = {6},
  number = {2},
  pages = {166--178},
  doi = {10.1007/s11704-012-2870-8},
  note = {Publication status: Published}
}

Kramer, O., & Gieseke, F. (2012). Evolutionary kernel density regression. Expert Systems and Applications, 39(10), 9246–9254.
More details BibTeX Full text DOI

@article{KramerG2012Evolutionary,
  author = {O Kramer and F Gieseke},
  title = {Evolutionary kernel density regression},
  journal = {Expert Systems and Applications},
  year = {2012},
  volume = {39},
  number = {10},
  pages = {9246--9254},
  doi = {10.1016/j.eswa.2012.02.080},
  url = {https://doi.org/10.1016/j.eswa.2012.02.080},
  note = {Publication status: Published}
}

Thesis (doctoral or post-doctoral)

Gieseke, F. (2012). From supervised to unsupervised support vector machines and applications in astronomy. at the Carl von Ossietzky University of Oldenburg.
More details BibTeX Full text

@phdthesis{Gieseke2012FromSupervised,
  author = {F Gieseke},
  title = {From supervised to unsupervised support vector machines and applications in astronomy},
  school = {Carl von Ossietzky University of Oldenburg},
  year = {2012},
  url = {http://oops.uni-oldenburg.de/1318/},
  note = {Publication status: Published}
}

2011

Research article in proceedings (conference)

Gieseke, F., Kramer, O., Airola, A., & Pahikkala, T. (2011). Speedy Local Search for Semi-Supervised Regularized Least-Squares. In Bach, J., & Edelkamp, S. (Eds.), KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Berlin, Germany, October 4-7,2011. Proceedings (pp. 87–98). Lecture Notes in Computer Science: Vol. 7006. Springer.
More details BibTeX Full text DOI

@inproceedings{GiesekeKAP2011SpeedyLocal,
  author = {F Gieseke and O Kramer and A Airola and T Pahikkala},
  title = {Speedy Local Search for Semi-Supervised Regularized Least-Squares},
  booktitle = {KI 2011: Advances in Artificial Intelligence, 34$^{th}$ Annual German Conference on AI, Berlin, Germany, October 4-7,2011. Proceedings},
  pages = {87--98},
  series = {Lecture Notes in Computer Science},
  volume = {7006},
  editor = {J Bach and S Edelkamp},
  year = {2011},
  publisher = {Springer},
  address = {Berlin, Germany},
  doi = {10.1007/978-3-642-24455-1\_8},
  url = {https://doi.org/10.1007/978-3-642-24455-1\_8},
  note = {Publication status: Published}
}

Kramer, O., & Gieseke, F. (2011). Analysis of wind energy time series with kernel methods and neural networks. In Ding, Y., Wang, H., Xiong, N., Hao, K., & Wang, L. (Eds.), Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011 (pp. 2381–2385). IEEE.
More details BibTeX Full text DOI

@inproceedings{KramerG2011AnalysisOf,
  author = {O Kramer and F Gieseke},
  title = {Analysis of wind energy time series with kernel methods and neural networks},
  booktitle = {Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011},
  pages = {2381--2385},
  editor = {Y Ding and H Wang and N Xiong and K Hao and L Wang},
  year = {2011},
  publisher = {IEEE},
  address = {Shanghai, China},
  doi = {10.1109/ICNC.2011.6022597},
  url = {https://doi.org/10.1109/ICNC.2011.6022597},
  note = {Publication status: Published}
}

Kramer, O., & Gieseke, F. (2011). Variance Scaling for EDAs Revisited. In Bach, J., & Edelkamp, S. (Eds.), KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Berlin, Germany, October 4-7,2011. Proceedings (pp. 169–178). Lecture Notes in Computer Science: Vol. 7006. Springer.
More details BibTeX Full text DOI

@inproceedings{KramerG2011VarianceScaling,
  author = {O Kramer and F Gieseke},
  title = {Variance Scaling for EDAs Revisited},
  booktitle = {KI 2011: Advances in Artificial Intelligence, 34$^{th}$ Annual German Conference on AI, Berlin, Germany, October 4-7,2011. Proceedings},
  pages = {169--178},
  series = {Lecture Notes in Computer Science},
  volume = {7006},
  editor = {J Bach and S Edelkamp},
  year = {2011},
  publisher = {Springer},
  address = {Berlin, Germany},
  doi = {10.1007/978-3-642-24455-1\_16},
  url = {https://doi.org/10.1007/978-3-642-24455-1\_16},
  note = {Publication status: Published}
}

Kramer, O., & Gieseke, F. (2011). Short-Term Wind Energy Forecasting Using Support Vector Regression. In Corchado, E., Sn{á}sel, V., Sedano, J., Hassanien, A., Calvo{-}Rolle, J., & Slezak, D. (Eds.), Soft Computing Models in Industrial and Environmental Applications, 6th International Conference {SOCO} 2011, 6-8 April, 2011, Salamanca, Spain (pp. 271–280). Advances in Intelligent and Soft Computing: Vol. 87. Springer.
More details BibTeX Full text DOI

@inproceedings{KramerG2011ShortTermWind,
  author = {O Kramer and F Gieseke},
  title = {Short-Term Wind Energy Forecasting Using Support Vector Regression},
  booktitle = {Soft Computing Models in Industrial and Environmental Applications, 6$^{th}$ International Conference {SOCO} 2011, 6-8 April, 2011, Salamanca, Spain},
  pages = {271--280},
  series = {Advances in Intelligent and Soft Computing},
  volume = {87},
  editor = {E Corchado and V Sn{á}sel and J Sedano and AE Hassanien and JL Calvo{-}Rolle and D Slezak},
  year = {2011},
  publisher = {Springer},
  address = {Salamanca, Spain},
  doi = {10.1007/978-3-642-19644-7\_29},
  url = {https://doi.org/10.1007/978-3-642-19644-7\_29},
  note = {Publication status: Published}
}

2010

Research article in proceedings (conference)

Gieseke, F., Moruz, G., & Vahrenhold, J. (2010). Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), Sydney, Australia, 815–820.
More details BibTeX DOI

@inproceedings{GiesekeMV2010ResilientKD,
  author = {F Gieseke and G Moruz and J Vahrenhold},
  title = {Resilient K-d Trees: K-Means in Space Revisited},
  booktitle = {Proceedings of the 10$^{th}$ IEEE International Conference on Data Mining (ICDM)},
  pages = {815--820},
  year = {2010},
  address = {Sydney, Australia},
  doi = {10.1109/ICDM.2010.94},
  note = {Publication status: Published}
}

Gieseke, F., Polsterer, K., Thom, A., Zinn, P., Bomans, D., Dettmar, R.-J., Kramer, O., & Vahrenhold, J. (2010). Detecting Quasars in Large-Scale Astronomical Surveys. In Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA), Washington D.C., USA, 352–357.
More details BibTeX DOI

@inproceedings{GiesekePTZBDKV2010DetectingQuasars,
  author = {F Gieseke and KL Polsterer and A Thom and P Zinn and D Bomans and R-J Dettmar and O Kramer and J Vahrenhold},
  title = {Detecting Quasars in Large-Scale Astronomical Surveys},
  booktitle = {Proceedings of the 9$^{th}$ International Conference on Machine Learning and Applications (ICMLA)},
  pages = {352--357},
  year = {2010},
  address = {Washington D.C., USA},
  doi = {10.1109/ICMLA.2010.59},
  note = {Publication status: Published}
}

Research article (journal)

Gieseke, F., Gudmundsson, J., & Vahrenhold, J. (2010). Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies. Journal of Discrete Algorithms (JDA), 8(3), 259–272.
More details BibTeX DOI

@article{GiesekeGV2010PruningSpanners,
  author = {F Gieseke and J Gudmundsson and J Vahrenhold},
  title = {Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies},
  journal = {Journal of Discrete Algorithms (JDA)},
  year = {2010},
  volume = {8},
  number = {3},
  pages = {259--272},
  doi = {10.1016/j.jda.2010.03.001},
  note = {Publication status: Published}
}

@article{GiesekeGV2010PruningSpanners,
  author = {Fabian Gieseke and Joachim Gudmundsson and Jan Vahrenhold},
  title = {Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies},
  journal = {Journal of Discrete Algorithms},
  year = {2010},
  volume = {8},
  number = {2},
  pages = {259--272},
  doi = {10.1016/j.jda.2010.03.001},
  note = {Publication status: Published}
}

2009

Research article in proceedings (conference)

Gieseke, F., Pahikkala, T., & Kramer, O. (2009). Fast evolutionary maximum margin clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Canada, 361–368.
More details BibTeX Full text DOI

@inproceedings{GiesekePK2009FastEvolutionary,
  author = {F Gieseke and T Pahikkala and O Kramer},
  title = {Fast evolutionary maximum margin clustering},
  booktitle = {Proceedings of the 26$^{th}$ International Conference on Machine Learning (ICML 2009)},
  pages = {361--368},
  series = {ACM International Conference Proceeding Series},
  volume = {382},
  year = {2009},
  publisher = {ACM},
  address = {Montreal, Canada},
  doi = {10.1145/1553374.1553421},
  url = {https://doi.org/10.1145/1553374.1553421},
  note = {Publication status: Published}
}