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THESEUS-Basistechnologien:
Maschinelles Lernen

 

Organisationen:

  • Siemens AG
  • Fraunhofer-Institut für Rechnerarchitektur und Softwaretechnik (FhG-FIRST)
  • Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme (FhG-IAIS)
  • Ludwig-Maximilians-Universität (LMU)

Kontakt:

Dr. Volker Tresp
Siemens AG
Otto-Hahn-Ring 6
81730 München
E-Mail: volker.tresp@siemens.com

Zielsetzung:

Das Thema des THESEUS-Basistechnologie-Cluster „Maschinelles Lernen” sind Verfahren der intelligenten Datenanalyse, die das automatische Erkennen von Zusammenhängen und Abhängigkeiten in den Daten ermöglichen, sodass diese ähnlich wie mit Hilfe von Ontologien modelliert und strukturiert werden können. Diese Methoden werden auf Texte, Bilder sowie auf Audio- und Videodaten angewendet und erlauben es auch, Beziehungen zwischen den verschiedenen Datentypen herzuleiten.

Präsentationen:

2009

  • A. Binder, M. Kawanabe: Enhancing Recognition of Visual Concepts with Primitive Color Histograms via Non-sparse Multiple Kernel Learning. In: Post Workshop Proceedings of CLEF, Corfu, 2009. (Download)
  • A. Binder, M. Kawanabe: ImageCLEF 2009 Photo Annotation Task Non-sparse Multiple Kernel Learning. CLEF Workshop, Corfu, 1.10.2009. (Download)

 2008

  • H. Kinnemann, G. Paaß: Technologien für die Multimediathek der Zukunft. Statusseminar CTC WP6, 27.11.2008 (Download)
  • L. Molzberger, G. Paaß, Z. Xu: Beiträge zum Contentus Demonstrator.
    Statusseminar CTC WP6, 27.11.2008 (Download)
  • T. Horvath, L. Molzberger, G. Paaß, F. Reichartz: Statistical Machine Learning. Statusseminar CTC WP6, 27.11.2008. (Download)
  • A. Rettinger, Y. Huang, V. Tresp: Task 6.1: Learning with Relational Data and Ontologies. Statusseminar CTC WP6, 27.11.2008 (Download)
  • M. Kawanabe, A. Binder, K.-R. Müller: Image Representations and Feature Combination. Statusseminar CTC WP6, 27.11.2008 (Download)
  • F. Graf: Learning Semantic Annotations from Images Managing and Querying Large Sets of Semantically Described Image Contents. Statusseminar CTC WP6, 27.11.2008 (Download)
  • M.-P. Schambach: Nutzung von Kontext in der Handschrifterkennung Statusseminar CTC WP6, 27.11.2008 (Download)
  • A. Stoffel: Strukturerkennung in Dokumentkollektionen. Statusseminar CTC WP6, 27.11.2008 (Download)
  • S. Grosman: Optimieren großer Parameterräume für Annotationssysteme. Statusseminar CTC WP6, 27.11.2008 (Download)
  • V. Tresp, K. Yu: Learning with Dependencies between Several Response Variables:
    From Hierarchical Bayes and Multitask Learning to Structured Output Prediction and Relational Learning. 26th International Conference on Machine Learning (ICML 2009) Montreal, 14.-18. Juni 2009 (Download)

Teilnahme an internationalen Wettbewerben:

2007

  • V. Märgner, H. El Abed: Arabic Handwriting Recognition Competition. In: Proceedings of the 9th Intl. Conf. on Document Analysis and Recognition (ICDAR), Curitiba (Brazil), 2007.

2009

  • E. Grosicki, H. El Abed: ICDAR 2009 Handwriting Recognition Competition. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona, Spain, 2009.

Wissenschaftliche Publikationen:

2010

  • Z. Li, M. Schulte-Austum, M. Neschen: Fast Logo Detection and Recognition in Document Images. In: Proceedings of the 20th International Conference in Pattern Recognition, Istanbul, Turkey, 2010.
  • A. Stoffel, D. Spretke, H. Kinnemann, D. A. Keim: Enhancing Document Structure Analysis using Visual Analytics. In: Proceedings of the 2010 ACM symposium on Applied Computing, 2010. (Download)
  • T. Emrich, F. Graf, H.-P. Kriegel, M. Schubert, M. Thoma, A. Cavallaro: CT Slice Localization via Instance-Based Regression. In: Proceedings of SPIE, Vol. 7623, 762320 (2010). (Download)
  • T. Emrich, F. Graf, H.-P. Kriegel, M. Schubert, M. Thoma, A. Cavallaro: On the Impact of Flash SSDs on Spatial Indexing . In: Proceedings of the Sixth International Workshop on Data Management on New Hardware (DaMoN 2010), Indianapolis, 2010. (Download)
  • T. Bernecker, T. Emrich, F. Graf, H.-P. Kriegel, P. Kröger, M. Renz, E. Schubert, A. Zimek: Subspace Similarity Search Using the Ideas of Ranking and Top-k Retrieval. In Proceedings of the 26th International Conference on Data Engineering (ICDE) Workshop on Ranking in Databases (DBRank), Long Beach, 2010. (Download)
  • F. Graf, H.-P. Kriegel, M. Renz, M. Schubert: PAROS: Pareto Optimal Route Selection. At: ACM SIGMOD/PODS Conference, Indianapolis, 2010. (Download)
  • M. Schubert, T. Emrich, H.-P. Kriegel, M. Thoma, F. Graf: Similarity Estimation using Bayes Ensembles. In: Proceedings of the 22nd International Conference on Scientific and Statistical Database Managment (SSDBM 2010), Heidelberg (to appear) (Download)
  • T. Bernecker, T. Emrich, F. Graf, H.-P. Kriegel, P. Kröger, M. Renz, E. Schubert,
    A. Zimek: Subspace Similarity Search Efficient k-NN Queries in Arbitrary Subspaces
    In: Proceedings of the 22nd International Conference on Scientific and Statistical Database Managment (SSDBM 2010), Heidelberg (to appear) (Download)
  • T. Emrich, F. Graf, H. P. Kriegel, M. Schubert, M. Thoma: Optimizing All-Nearest-Neighbor Queries with Trigonometric Pruning. In: Proceedings of the 22nd International Conference on Scientific and Statistical Database Managment (SSDBM 2010), Heidelberg (to appear) (Download)
  • M: Sugiyama, M. Kawanabe, P. L. Chui: Dimensionality Reduction for Density Ratio Estimation in High-dimensional Spaces. In: Neural Networks, vol.23, no.1, pp.44-59, 2010. (Download)
  • M. Sugiyama, S. Hara, P. v. Bünau, T. Suzuki, T. Kanamori, M. Kawanabe: Direct Density Ratio Estimation with Dimensionality Reduction. At: SIAM International Conference on Data Mining, 2010. (Download)
  • A. Binder, M. Kawanabe: Enhancing Recognition of Visual Concepts with Primitive Color Histograms via Non-sparse Multiple Kernel Learning. In: Post Workshop Proceedings of CLEF, Corfu, 2009. (Download)

2009

  • A. Stoffel, E. Tapia, R. Rojas: Recognition of On-Line Handwritten Commutative Diagrams. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona, Spain, 2009.
  • M.-P. Schambach: Recurrent HMMs and Cursive Handwriting Recognition Graphs. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona, Spain, 2009. (Download)
  • H. Strobelt, D. Oelke, C. Rohrdantz, A. Stoffel, D. A. Keim, O. Deussen: Document Cards: A Top Trumps Visualization for Documents. In:IEEE Symposium on Information Visualization (InfoVis), Atlantic City, USA, 2009.
  • A. Binder, M. Kawanabe, U. Brefeld: Efficient Classification of Images
    with Taxonomies. In: Proc. 9th Asian Conference on Computer Vision (ACCV’09), Xi’an, China, Springer-Verlag (2009) (Download)
  • A. Binder, M. Kawanabe: Non-sparse Multiple Kernel Learning. At: ImageCLEF2009 Photo Annotation Task. (Download)
  • A. Binder, M. Kawanabe, M. Kloft, S. Nakajima: Enhancing Image Annotation with Primitive Color Histograms via Non-sparse Multiple Kernel Learning. At: NIPS Workshop on Understanding Multiple Kernel Learning Methods, 2009. (Download)
  • M. Boley, T. Horvath, S. Wrobel: Efficient Discovery of Interesting Patterns Based on Strong Closedness. In: Proceeding of the SIAM International Conference for Data Mining (SDM). (Download)
  • A. Rettinger, M. Nickles, V. Tresp: Statistical Relational Learning with Formal Ontologies. In: Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2009. (Download)
  • A. Rettinger, M. Nickles: Infinite Hidden Semantic Models for Learning with OWL DL. In: Proceedings of 1st ESWC Workshop on Inductive Reasoning and Machine Learning on the Semantic Web 2009. (Download)
  • V. Tresp, Y. Huang, M. Bundschus, A. Rettinger: Materializing and Querying Learned Knowledge. In: Proceedings of 1st ESWC Workshop on Inductive Reasoning and Machine Learning on the Semantic Web 2009. (Download)
  • T. Horvath, G. Paass, F. Reichartz, S. Wrobel: A Logic-Based Approach to Relation Extraction from Texts.In: W. Buntine, M. Grobelnik, D. Mladenic, J. Shawe-Taylor (Eds.). Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II. (Download)
  • Z. Xu, K. Kersting, V. Tresp: Multi-relational learning with gaussian processes
    In: The 21st International Joint Conference on Artificial Intelligence (IJCAI-09), 11-17 JULY 2009, Pasadena, CA, USA. (Download)
  • A. Binder, M. Kawanabe: Non-sparse Multiple Kernel Learning. At: ImageCLEF, Corfu, 2009. (Download)
  • Z. Xu, V. Tresp, A. Rettinger, K. Kersting: Social network mining with nonparametric relational models. In: H. Zhang, M. Smith, L. Giles, J. Yen (eds.). Advances in Social Network Mining and Analysis, Springer, 2009. (Download)
  • M. Kawanabe, S. Nakajima, A. Binder: A Procedure of Adaptive Kernel Combination
    with Kernel-Target Alignment for Object Classification. In: S. Marchand-Maillet & Y. Kompatsiaris (Eds.). CIVR , ACM. (Download)
  • S. Nakajima, A. Binder, U. Brefeld, K.-R. Müller, M. Kawanabe: Non-sparse Feature Mixing in Object Classification. In: Information Processing Society Japan SIG Technical Report. (Download)
  • S. Nakajima, A. Binder, C. Müller, W. Wojcikiewicz, M. Kloft, U. Brefeld, K.-R. Müller, M. Kawanabe: Multiple Kernel Learning for Object Classification. In: Technical Report on Information-Based Induction Sciences, 2009. (Download)
  • G. Paaß, S. Eickeler, S. Wrobel: Text Mining and Multimedia Search in a Large
    Content Repository. In: Proceedings of the Sabre Conference on Text Minng Services (TMS ’09), Leipzig, 2009, pp. 15-22. (Download)
  • G. Paaß, I. Konya: Machine Learning for Document Structure Recognition. (Download)
  • G. Paaß, A. Pilz, J. Schwenninger: Named Entity Recognition of Spoken Documents using Subword Units. In: Institute of Electrical and Electronics Engineers: ICSC 2009: third IEEE International Conference on Semantic Computing, Berkeley, 2009. IEEE, 2009, pp. 529-534 (Download)
  • G. Paaß, F. Reichartz: Exploiting Semantic Constraints for Estimating Supersenses with CRFs. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2009, Sparks, Nevada, pp. 485-496. (Download)
  • P. v. Bünau, F. C. Meinecke, F. C. Kiraly, K.-R. Müller: Finding Stationary Subspaces in Multivariate Time Series. In: Physical Review Letters, Vol. 103, No. 21. (Nov 2009), 214101. (Download)
  • A. Pilz, L. Molzberger, G. Paaß: Entity Resolution by Kernel Methods. In: Heyer, Gerhard (Ed.). Text Mining Services – Building and applying text mining based service infrastructures in research and industry: building and applying text mining based service infrastructures in research and industry. Leipzig: University, 2009. (Leipziger Beiträge zur Informatik 14), pp. 71-80. (Download)
  • A. Pilz, G. Paaß: Named Entity Resolution Using Automatically Extracted Semantic Information. In: Benz, Dominik (Ed.) et al.. KDML 2009: Workshop on Knowledge Discovery, Data Mining, and Machine Learning (KDML), Darmstadt, 2009, pp. 84-91. (Download)
  • F. Reichartz, H. Korte, G. Paass: Composite Kernels For Relation Extraction. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers. Association for Computational Linguistics, Suntec, Singapore , pp. 365–368 . (Download)
  • Y. Huang, V. Tresp, M. Bundschus, A. Rettinger: Scalable Relational Learning for Sparse and Incomplete Domains. In: Proceedings of the International Workshop on Statistical Relational Learning (SRL-2009), 2009. (Download)

2008

  • K. Worm, B. Meffert: Surface Modifications for Robust Image Based Mail Piece Comparison. In: The Eighth IAPR International Workshop on Document Analysis Systems, 2008, pp. 637-643.
  • M.-P. Schambach, J. Rottland, T. Alary: How to Convert a Latin Handwriting Recognition System to Arabic. In: Proceedings of The 11th International Conference on Frontiers in Handwriting Recognition, Montréal, Québec, 2008. (Download)
  • U. Miletzki: Genesis of Postal Address Reading, Current State and Future Prospects: Thirty Years of Pattern Recognition on Duty of Postal Services. In: Y. Li, B. Liu, S. Sarawagi (Eds.). Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, 2008. (Download)
  • K. Worm, B. Meffert: Robust Image Based Document Comparison Using Attributed Relational Graphs. In: Proceedings of the 5th International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA), Acta Press, Innsbruck, pp. 116-121, 02/2008.
  • T. Schreck, M. Schüßler, K. Worm, F. Zeilfelder: Butterfly Plots for Visual Analysis of Large Point Cloud Data. In: S. Cunningham (Ed.). Proceedings of 16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision.WSCG 2008, Czech Republic, pp. 33-40, 2008.
  • A. Rettinger, M. Nickles, V. Tresp: A statistical relational model for trust learning. In: L. Padgham, D. Parkes, J. P. Muller (Eds.). Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (Aamas 2008) – Volume 2. (Download)
  • M. Bundschus, M. Dejori, Sh. Yu, V. Tresp, H-P. Kriegel: Statistical modeling of medical indexing processes for biomedical knowledge information discovery from text.  At: International Workshop on Data Mining in Bioinformatics (BIOKDD ’08). (Download)
  • M. Bundschus, M. Dejori, M. Stetter, V. Tresp, H-P. Kriegel: Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, Vol. 9, No. 1. (2008), 207. (Download)
  • M. L. Braun, J. M. Buhmann, K.-R. Müller: On Relevant Dimensions in Kernel Feature Spaces. In: P. Bartlett (Ed.). Journal of Machine Learning Research 9 (2008), pp. 1875-1908. (Download)
  • T. Horváth, J. Ramon: Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth. In: Lecture Notes In Artificial Intelligence; Vol. 5211. Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases – Part . Springer-Verlag Berlin, Heidelberg. pp. 520-535. (Download)
  • Z. Xu, V. Tresp, S. Yu, K. Yu: Nonparametric relational learning for social network analysis. In: 2nd ACM Workshop on Social Network Mining and Analysis (SNA-KDD 2008). (Download)
  • C. Lippert, S. Weber, Y. Huang, V. Tresp, M. Schubert, H. P. Kriegel: Relation-Prediction in Multi-Relational Domains using Matrix-Factorization. In: NIPS 2008 Workshop. Structured Input – Structured Output. (Download)
  • S. Reckow, V. Tresp: Integrating Ontological Prior Knowledge into Relational Learning. In: NIPS 2008 Workshop. Structured Input – Structured Output. (Download)
  • F. Reichartz, G. Paaß: Estimating Supersenses with Conditional Random Fields. At: ECML-Workshop HLIE 08. (Download)
  • V. Tresp, M. Bundschus, A. Rettinger, Y. Huang: Towards Machine Learning on the Semantic Web. In: Uncertainty Reasoning for the Semantic Web I, LNCS, Springer. pp. 282-314. (Download)
  • M. Sugiyama, T. Suzuki, S. Nakajima, H. Kashima, P. v. Bunau, M. Kawanabe: Direct Importance Estimation for Covariate Shift Adaptation. In: Annals of the Institute of Statistical Mathematics, Vol.60 (2008), No.4, pp.699-746. (Download)

2007

  • A. Rettinger, M. Nickles, V. Tresp: Learning initial trust among interacting agents. International Workshop CIA on Cooperative Information Agents, Delft, 2007. (Download)
  • S. Oba, M. Kawanabe, K. R. Müller, S. Ishii: Heterogeneous Component Analysis. At: NIPS Conference 2007. (Download)
  • M. Kawanabe, M. Sugiyama, G. Blanchard, K.-R. Müller: A New Algorithm of Non-Gaussian Component Analysis with Radial Kernel Functions. In: Annals of the Institute of Ststistical Mathematics, vol.59, no.1, pp. 57-75, 2007. (Download)
  • Z. Xu, V. Tresp, S. Yu, , K. Yu, , H.-P. Kriegel, : Fast Inference in Infinite Hidden Relational Models. At: Workshop on Mining and Learning with Graphs, Firenze, Italy, 2007. (Download)
  • K. Yu, W. Chu, S. Yu, V. Tresp, Z. Xu: Stochastic Relational Models for Discriminative Link Prediction. In: Advances in Neural Information Processing Systems. MIT Press, 2007, pp. 333-340 (Download)
  • K. Yamazaki, M. Kawanabe, S. Watanabe, M. Sugiyama, K.-R. Müller: Asymptotic Bayesian Generalization Error When Training and Test Distributions Are Different. In: Z. Ghahramani (Ed.), Proceedings of the 24th Annual International Conference on Machine Learning, Omnipress, Corvallis, OR, 2007, pp. 1079-1086. (Download)

 

Software, Softwarebeschreibungen und Dokumentationen:

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Online-Demos:

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Weitere Informationen:

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Wichtige Hyperlinks:

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