Browse all publications by topic

• Models of Neural Systems
• Analysis of Neural Data
• Machine Learning and Neural Networks
• Other Signal Processing Applications
• Miscellaneous

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• 2009 - 2008 - 2007 - 2006 - 2005 - 2004 - 2003 - 2002 - 2001 - 2000 - 19xx - ALL

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Machine Learning and Neural Networks

[ Journals | Book Chapters | Conf. Proceedings | Selected Abstracts | Theses and Tech. Reports ]

Publications in Journals

• A. Heinz, A. Beck, J. Wrase, J. Mohr, K. Obermayer, J. Gallinat, and I. Puls. Neurotransmitter systems in alcohol dependence. Pharmacopsychiatry, 42(S 01):S95-S101, 2009.
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• J. Mohr, I. Puls, J. Wrase, S. Vollstaedt-Klein, T. Leménager, C. Vollmert, M. Rapp, K. Obermayer, A. Heinz, and M. Smolka. A model comparison of COMT effects on central processing of affective stimuli. Neuroimage, 2009. in press.
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• F.-F. Henrich and K. Obermayer. Learning by spherical subdivision. Journal of Machine Learning Research, 9:105-130, 2008.
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• T. Knebel, S. Hochreiter, and K. Obermayer. An SMO algorithm for the potential support vector machine. Neural Computation, 20(1):271-287, 2008.
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• F. Lohoff, M. Lautenschlager, J. Mohr, T. Ferraro, T. Sander, and J. Gallinat. Association between variation in the vesicular monoamine transporter 1 gene on chromosome 8p and anxiety-related personality traits. Neuroscience Letters, 434:41-45, 2008.
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• J. Mohr, B. Jain, and K. Obermayer. Molecule kernels: A descriptor- and alignment-free QSAR approach. Journal of Chemical Information and Modeling, 48(9):1868-1881, 2008.
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• J. Mohr, I. Puls, J. Wrase, J. Priller, J. Behr, W. Kitzrow, N. Makris, H. Breiter, K. Obermayer, and A. Heinz. Synergistic effects of the dopaminergic and glutamatergic system on hippocampal volume in alcohol-dependent patients. Biological Psychology, 79(1), 2008.
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• S. Hochreiter, M. Heusel, and K. Obermayer. Fast model-based protein homology detection without alignment. Bioinformatics, 23(14):1728-1736, 2007.
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• J. Hochreiter and K. Obermayer. Support vector machines for dyadic data. Neural Comput., 18:1472-1510, 2006.
  (Abstract HTML) (FTP PDF, 416 kb)

• S. Hochreiter, D-A. Clevert, and K. Obermayer. A new summarization method for affymetrix probe level data. Bioinformatics, 22:943-949, 2006.
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• R. Vollgraf and K. Obermayer. Quadratic optimization for simultaneous matrix diagonalization. IEEE Trans. Signal Processing, 54(9):3270-3278, 2006.
  (Abstract HTML) (FTP PDF, 477 kb)

• S. Seo and K. Obermayer. Self-organizing maps and clustering methods for matrix data. Neural Networks Special Issue, 17:1211-1229, 2004. Unfortunately, there are some typos in the equation (11)-(13) of this paper. Please see the file ERRATUM for the correction.
  (Abstract HTML) (FTP PDF, 432 kb)

• S. Seo and K. Obermayer. Soft learning vector quantization. Neural Comput., 15:1589-1604, 2003.
  (Abstract HTML) (FTP PostScript, 13 pages, 582 kb)

• S. Seo, M. Bode, and K. Obermayer. Soft nearest prototype classification. IEEE Trans. Neur. Netw., 14:390-398, 2003.
  (Abstract HTML) (FTP PostScript, 12 pages, 446 kb)

• R. Krepki, Y. Pe, H. Meng, and K. Obermayer. A new algorithm for the interrogation of 3D holographic PTV data based on deterministic annealing and EM-optimization. Exp. Fluids (Suppl.), pages 99-107, 2000.
  (Abstract HTML) (FTP Gzipped PostScript, 244 kb)

• M. Stellmacher and K. Obermayer. A new particle tracking algorithm based on deterministic annealing and alternative distance measures. Exp. Fluids, 28:506-518, 2000.
  (Abstract HTML) (FTP Gzipped PostScript, 14 pages, 1027 kb)

• T. Graepel and K. Obermayer. A self-organizing map for proximity data. Neural Comput., 11:139-155, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 17 pages, 87 kb)

• T. Graepel, M. Burger, and K. Obermayer. Self-organizing maps: generalizations and new optimization techniques. Neurocomputing, 20:173-190, 1998.
  (Abstract HTML) (FTP Gzipped PostScript, 21 pages, 116 kb)

• T. Graepel, M. Burger, and K. Obermayer. Phase transitions in stochastic self-organizing maps. Phys. Rev. E, 56:3876-3890, 1997.
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• E. Erwin, K. Obermayer, and K. Schulten. Self-organizing maps: ordering, convergence properties and energy functions. Biol. Cybern., 67:47-55, 1992.
  (Abstract HTML) (FTP Gzipped PostScript, 26 pages, 84 kb)

• E. Erwin, K. Obermayer, and K. Schulten. Self-organizing maps: stationary states, metastability and convergence rate. Biol. Cybern., 67:35-45, 1992.
  (Abstract HTML) (FTP Gzipped PostScript, 32 pages, 123 kb)

Publications in Books

• S. Hochreiter and K. Obermayer. Nonlinear feature selection with the potential support vector machine. In I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, Feature Extraction: Foundations and Applications, pages 419-438. Springer Publishers, 2006.

• S. Hochreiter and K. Obermayer. Gene selection for microarray data. In B. Schölkopf, K. Tsuda, and J.-P. Vert, editors, Kernel Methods in Computational Biology, pages 319-356. MIT Press, Cambridge, Massachusetts, 2004.
  (Abstract HTML) (FTP Gzipped PostScript, 52 pages, 374 kb)

• R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 115-132. MIT Press, 2000.
  (Abstract HTML) (FTP Gzipped PostScript, 61 pages, 588 kb)

• M. Hasenjäger, H. Ritter, and K. Obermayer. Active learning in self-organizing maps. In Kohonen Maps, pages 57-70. Elsevier, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 14 pages, 675 kb)

• R. Herbrich, M. Keilbach, T. Graepel, P. Bollmann-Sdorra, and K. Obermayer. Neural networks in economics: Background, applications and new developments. In Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics, volume 11, pages 169-196. Kluwer Academics, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 27 pages, 102 kb)

• H. Ritter, K. Obermayer, and J. Rubner. Self-organizing maps and adaptive filters. In Physics of Neural Networks, pages 281-306. Springer, New York, 1991.

Publications in Conference Proceedings

• S. Seo, J. Mohr, and K. Obermayer. A new incremental pairwise clustering algorithm. In Proceedings of the ICMLA -09: The Eighth International Conference on Machine Learning and Applications, Los Alamitos, CA, USA, 2009. IEEE Computer Society. (accepted).
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• J. Jain and K. Obermayer. On the sample mean of graphs. In IJCNN 2008 Conference Proceedings, pages 993-1000, 2008.

• J. Mohr, S. Seo, and K. Obermayer. Automated microarray classification based on P-SVM gene selection. In Proceedings of the ICMLA '08: The Seventh International Conference on Machine Learning and Applications, pages 503-507, 2008. The first two authors contributed equally.
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• J. Mohr, S. Seo, I. Puls, A. Heinz, and K. Obermayer. Target selection: A new learning paradigm and its application to genetic association studies. In Proceedings of the ICMLA '08: The Seventh International Conference on Machine Learning and Applications, pages 182-187, 2008.
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• N. Neubauer, C. Scheel, S. Albayrak, and K. Obermayer. Distance measures in query space: How strongly to use feedback from past queries. In Proceedings of the IEEE Conference on Web Intelligence 2007, pages 607-613, 2007.

• C. Scheel, N. Neubauer, A. Lommatzsch, K. Obermayer, and S. Albayrak. Efficient query delegation by detecting redundant retrieval strategies. In SIGIR Workshop on Learning to Rank for Information Retrieval 2007, 2007.
  (Abstract HTML) (FTP PDF, 2739 kb)

• J. Mohr, I. Puls, J. Wrase, S. Hochreiter, A. Heinz, and K. Obermayer. P-SVM variable selection for discovering dependencies between genetic and brain imaging data. In IJCNN 2006 Conference Proceedings, pages 5020-5027, 2006.
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• S. Seo and K. Obermayer. Dynamic hyperparameter scaling method for LVQ algorithms. In IJCNN 2006 Conference Proceedings, pages 3196-3203, 2006.
  (Abstract HTML) (FTP PDF, 222 kb)

• R. Vollgraf and K. Obermayer. Sparse optimization for second order kernel methods. In IJCNN 2006 Conference Proceedings, pages 145-152, 2006.
  (Abstract HTML) (FTP PDF, 1188 kb)

• S. Hochreiter and K. Obermayer. Optimal gradient-based learning using importance weights. In Proceedings of the International Joint Conference on Neural Networks, volume 1, pages 114-119, 2005.

• S. Hochreiter and K. Obermayer. Optimal kernels for unsupervised learning. In Proceedings of the International Joint Conference on Neural Networks, volume 3, pages 1895-1899, 2005.
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• J. Mohr and K. Obermayer. A topographic support vector machine: Classification using local label configurations. In Saul L., Weiss Y., and Bottou L., editors, Advances in Neural Information Processing Systems 17, pages 929-936. MIT Press, 2005.

• R. Vollgraf, M. Scholz, I. Meinertzhagen, and K. Obermayer. Nonlinear filtering of electron micrographs by means of support vector regression. In Advances in Neural Information Processing Systems 16, pages 717-724, Cambridge, Massachusetts, 2004. MIT Press.
  (Abstract HTML) (FTP PDF, 451 kb)

• E. Cuadros-Vargas, R. Romero, and K. Obermayer. Speeding up algorithms of the SOM family for large and high dimensional databases. In Yamakawa T., editor, Proceedings WSOM, pages 167-172, 2003.
  (Abstract HTML) (FTP PDF, 244 kb)

• S. Hochreiter and K. Obermayer. Feature selection and classification on matrix data: from large margins to small covering numbers. In Advances in Neural Information Processing Systems 15, pages 913-920, Cambridge, Massachusetts, 2003. MIT Press.
  (Abstract HTML) (FTP Gzipped PostScript, 8 pages, 164 kb)

• S. Hochreiter, M. Mozer, and K. Obermayer. Coulomb classifiers: generalizing support vector machines via an analogy to electrostatic systems. In Advances in Neural Information Processing Systems 15, pages 561-568, Cambridge, Massachusetts, 2003. MIT Press.
  (Abstract HTML) (FTP Gzipped PostScript, 8 pages, 178 kb)

• R. Vollgraf and K. Obermayer. Multi dimensional ICA to separate correlated sources. In Advances in Neural Information Processing Systems 14, pages 993-1000, Cambridge, Massachusetts, 2002. MIT Press.
  (Abstract HTML) (FTP Gzipped PostScript, 8 pages, 514 kb)

• R. Vollgraf, I. Schießl, and K. Obermayer. Blind source separation of single components from linear mixtures. In Proc. Int. Conf. on Artificial Neural Networks - ICANN 01, pages 509-514, 2001.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 339 kb)

• T. Graepel, R. Herbrich, and K. Obermayer. Bayesian transduction. In Advances in Neural Information Processing Systems 12, pages 456-462, Cambridge, Massachusetts, 2000. MIT Press.
  (Abstract HTML) (FTP Gzipped PostScript, 7 pages, 45 kb)

• I. Schießl, H. Schöner, M. Stetter, A. Dima, and K. Obermayer. Regularized second order source separation. In P. Pajunen and J. Karhunen, editors, Int. Workshop on Independent Component Analysis and Blind Signal Separation, volume 2, pages 111-116, 2000.
  (Abstract HTML) (FTP Gzipped PostScript, 5 pages, 351 kb)

• S. Seo, M. Wallat, T. Graepel, and K. Obermayer. Gaussian process regression: Active data selection and test point rejection. In Neural Networks - IJCNN 2000, pages 241-246, 2000.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 306 kb)

• R. Vollgraf, M. Stetter, and K. Obermayer. Convolutive decorrelation procedures for blind source separation. In P. Pajunen and J. Karhunen, editors, Int. Workshop on Independent Component Analysis and Blind Signal Separation, pages 515-520, 2000.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 110 kb)

• T. Graepel, R. Herbrich, P. Bollmann-Sdorra, and K. Obermayer. Classification on pairwise proximity data. In Advances in Neural Information Processing Systems 11, pages 438-444, Cambridge, Massachusetts, 1999. MIT Press.
  (Abstract HTML) (FTP Gzipped PostScript, 7 pages, 65 kb)

• T. Graepel, R. Herbrich, B. Schölkopf, A. Smola, P. Bartlett, K. R. Müller, K. Obermayer, and R. Williamson. Classification on proximity data with LP-machines. In 9th International Conference on Artificial Neural Networks - ICANN99, pages 304-309, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 42 kb)

• M. Hasenjäger, H. Ritter, and K. Obermayer. Active data selection for fuzzy topographic mapping of proximities. In Brewka G., Der R., Gottwald S., and Schierwagen A., editors, Fuzzy-Neuro Systems 1999 - Computational Intelligence, pages 93-104, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 11 pages, 78 kb)

• M. Hasenjäger, H. Ritter, and K. Obermayer. Active topographic mapping of proximities. In 9th International Conference on Artificial Neural Networks - ICANN99, pages 952-957, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 67 kb)

• R. Herbrich, T. Graepel, and K. Obermayer. Support vector learning for ordinal regression. In 9th International Conference on Artificial Neural Networks - ICANN99, pages 97-102, 1999.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 40 kb)

• M. Burger, T. Graepel, and K. Obermayer. An annealed self-organizing map for source channel coding. In Advances in Neural Information Processing Systems 10, pages 430-436, Cambridge, Massachusetts, 1998. MIT Press.
  (Abstract HTML) (FTP Gzipped PostScript, 7 pages, 584 kb)

• T. Graepel and K. Obermayer. Fuzzy topographic kernel clustering. In W. Brauer, editor, Proceedings of the 5th GI Workshop Fuzzy Neuro Systems, pages 90-97, 1998.
  (Abstract HTML) (FTP Gzipped PostScript, 8 pages, 69 kb)

• A. Kuzmanovski, M. Stellmacher, and K. Obermayer. A new algorithm for the evaluation of PTV data using point matching distance mesaures and deterministic annealing. In Proc. Int. Symp. on Appl. of Laser Techniques to Fluid Mechanics, pages 10.4.1-10.4.6, 1998.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 203 kb)

• M. Burger, T. Graepel, and K. Obermayer. Phase transitions in soft topographic vector quantization. In W. Gerstner, A. Germond, M. Hasler, and J. Nicoud, editors, Artificial Neural Networks - ICANN 97, pages 619-624. Springer-Verlag, 1997.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 71 kb)

• T. Graepel, M. Burger, and K. Obermayer. Deterministic annealing for topographic vector quantization and self-organizing maps. In T. Kohonen, editor, Proceedings of the Workshop on Self-Organizing Maps - WSOM 97, pages 345-350, 1997.
  (Abstract HTML) (FTP Gzipped PostScript, 6 pages, 101 kb)

• K. Obermayer. Neural pattern formation and self-organizing maps. In Annales de Groupe CARNAC 5, pages 91-104, 1992.

• E. Erwin, K. Obermayer, and K. Schulten. Convergence properties of self-organizing maps. In T. Kohonen et al., editors, Artificial Neural Networks I, pages 409-414. North Holland, 1991.

Selected Abstracts

• P. Kallerhoff, A. Hollaender, K. Obermayer, and J.-D. Haynes. Predictability of reward modulates spatial attention. In Society for Neuroscience Abstracts, 2008.
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• K. Obermayer. New methods for supervised and unsupervised learning. In Innovations in Classification, Data Science, and Information Systems, 27th Annual Conference of the German Classification Society, page 131, 2003.

Theses and Technical Reports

• S. Grünewälder and K. Obermayer. Optimality of LSTD and its relation to TD and MC. Technical report, Berlin University of Technology, 2006.
  (Abstract HTML) (FTP Gzipped PostScript, 30 pages, 327 kb)

• S. Hochreiter and K. Obermayer. Classification, regression, and feature selection on matrix data. Technical report, Technische Universität Berlin, Fakultät für Elektrotechnik und Informatik, 2004. (revised December 2004).
  (FTP PDF, 703 kb)