• 2022

    Forschungsartikel in Sammelband (Konferenz)

    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: Society for Industrial and Applied Mathematics (SIAM).
    More details BibTeX DOI

    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 Proceedings of the International Conference on Advances in Geographic Information Systems (SIGSPATIAL), Seattle, Washington. (accepted / in press (not yet published))
    More details BibTeX

    Forschungsartikel (Zeitschrift)

    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

    Mugabowindekwe, 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. (accepted / in press (not yet published))
    More details BibTeX

    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, 14(16), 3912.
    More details BibTeX DOI

    Forschungsartikel als ePaper (Konferenz)

    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

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    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 Proceedings of the IEEE Big Data 2021, Virtual Event. (accepted / in press (not yet published))
    More details BibTeX

    Abstract als ePaper (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

    Forschungsartikel (Zeitschrift)

    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

  • 2020

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    Oancea, C. E., Robroek, T., & Gieseke, F. (2020). Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees. In Proceedings of the IEEE International Conference on Big Data, Atlanta, GA, USA, 5172–5181.
    More details BibTeX DOI

    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

    Forschungsartikel (Zeitschrift)

    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

    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, 12(18).
    More details BibTeX DOI

  • 2019

    Forschungsartikel in Sammelband (Konferenz)

    Ko, V., Oehmcke, S., & Gieseke, F. (2019). Magnitude and Uncertainty Pruning Criterion for Neural Networks. In Proceedings of the IEEE International Conference on Big Data, Los Angeles, USA, 2317–2326.
    More details BibTeX Full text DOI

    Oehmcke, S., Thrysøe, C., Borgstad, A., Salles, M., Brandt, M., & Gieseke, F. (2019). Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. In Proceedings of the International Conference on Big Data, Los Angeles, USA, 2403–2412.
    More details BibTeX Full text DOI

  • 2018

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    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

    Forschungsartikel (Zeitschrift)

    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

    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

  • 2017

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    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

    Forschungsartikel (Zeitschrift)

    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

    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.
    More details BibTeX DOI

    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

    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

    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

    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

  • 2016

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    Forschungsartikel (Zeitschrift)

    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

  • 2015

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    Forschungsartikel (Zeitschrift)

    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

  • 2014

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    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

    Forschungsartikel (Zeitschrift)

    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

    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

  • 2013

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    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

    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

    Forschungsartikel (Zeitschrift)

    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

    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

    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

    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

    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

  • 2012

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    Forschungsartikel (Zeitschrift)

    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

    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

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

    Qualifikationsschrift (Dissertation, Habilitationsschrift)

    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

  • 2011

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    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

    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

  • 2010

    Forschungsartikel in Sammelband (Konferenz)

    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

    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

    Forschungsartikel (Zeitschrift)

    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

  • 2009

    Forschungsartikel in Sammelband (Konferenz)

    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