Dr. Kai Labusch

Photo of Dr. Kai  Labusch

Wissenschaftlicher Mitarbeiter

Institut für Neuro- und Bioinformatik
Ratzeburger Allee 160 (Geb. 64)
23562 Lübeck

Email:
Phone:
+49 451 500 5501
Fax:
+49 451 500 5502

Research interests 

  • Learning of sparse codes
  • Feature extraction using sparse codes
  • Fast and simple algorithms for supervised learning (SVM)

Publikationen

2012

  • Hocke, J., Labusch, K., Barth, E., and Martinetz, T.: Sparse Coding and Selected Applications: KI - Künstliche Intelligenz, vol. 26, no. 4, Springer Berlin / Heidelberg, pp. 349-355, 2012
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2011

  • Labusch, K., Barth, E., and Martinetz, T.: Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent: IEEE Transactions on Selected Topics in Signal Processing, vol. 5, no. 5, pp. 1048-1060, 2011
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  • Labusch, K., Barth, E., and Martinetz, T.: Soft-competitive Learning of Sparse Codes and its Application to Image Reconstruction: Neurocomputing, vol. 74, no. 9, pp. 1418-1428, 04 2011
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2010

  • Braenne, I., Labusch, K., and Mamlouk, A. M.: Sparse Coding for Feature Selection on Genome-wide Association Data: Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki,Greece, September 15-18, 2010, Proceedings, vol. 6352, Springer, pp. 337-346, 2010
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  • Braenne, I., Labusch, K., Martinetz, T., and Mamlouk, A. M.: Interpretive Risk Assessment on GWA Data with Sparse Linear Regression: Machine Learning Reports, pp. 61-68, 2010
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  • Labusch, K., Barth, E., and Martinetz, T.: Bag of Pursuits and Neural Gas for Improved Sparse Coding: Proceedings of the 19th International Conference on Computational Statistics, Saporta, G. (Ed.), Springer, pp. 327-336, 2010
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  • Labusch, K. and Martinetz, T.: Learning Sparse Codes for Image Reconstruction: Proceedings of the 18th European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), D-Side Publishers, pp. 241-246, 2010
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2009

  • Labusch, K., Barth, E., and Martinetz, T.: Sparse Coding Neural Gas: Learning of Overcomplete Data Representations: Neurocomputing, vol. 72, no. 7-9, pp. 1547-1555, 2009
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  • Labusch, K., Barth, E., and Martinetz, T.: Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas: Advances in Self-Organizing Maps - WSOM 2009, 7th International Workshop, St. Augustine, Fl, USA, June 2009, vol. 5629, Principe, J. and Miikkulainen, R. (Ed.), Springer, series Lecture Notes in Computer Science, pp. 145-153, 2009
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  • Labusch, K., Barth, E., and Martinetz, T.: Demixing Jazz-Music: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources: Neural Network World, vol. 19, no. 5, Institute of Information and Computer Technology ASCR; Faculty of Transport, Czech Polytechnic University, Prague, pp. 561-579, 2009
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  • Martinetz, T., Labusch, K., and Schneegass, D.: SoftDoubleMaxMinOver: Perceptron-like Training of Support Vector Machines: IEEE Transactions on Neural Networks, vol. 20, no. 7, pp. 1061-1072, 2009
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2008

  • Labusch, K., Barth, E., and Martinetz, T.: Learning data representations with Sparse Coding Neural Gas: Proceedings of the 16th European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), D-Side Publishers, pp. 233-238, 2008
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  • Labusch, K., Barth, E., and Martinetz, T.: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources: Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II, vol. 5163, Kurková, V., Neruda, R., and Koutn`ik, J. (Ed.), Springer, pp. 788-797, 2008
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  • Labusch, K., Barth, E., and Martinetz, T.: Simple Method for High-Performance Digit Recognition Based on Sparse Coding: IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1985-1989, 2008
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  • Labusch, K., Timm, F., and Martinetz, T.: Simple Incremental One-Class Support Vector Classification: Pattern Recognition - Proceedings of the DAGM, Rigoll, G. (Ed.), pp. 21-30, 2008
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2007

  • Labusch, K., Siewert, U., Martinetz, T., and Barth, E.: Learning optimal features for visual pattern recognition: Human Vision and Electronic Imaging XII, vol. 6492, Rogowitz, B. E., Pappas, T. N., and Daly, S. J. (Ed.), Proceedings of SPIE, 2007
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2006

  • Schneegass, D., Labusch, K., and Martinetz, T.: MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation: Artificial Neural Networks - ICANN 2006, Springer, Berlin, Heidelberg, pp. 150-158, 2006
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2005

  • Martinetz, T., Labusch, K., and Schneegass, D.: SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification.: Artificial Neural Networks: Biological Inspirations. ICANN 2005: 15th International Conference. Proceedings, Part II, Duch, W., Kacprzyk, J., Oja, E., and Zadrozny, S. (Ed.), pp. 301-306, 2005
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2004

  • Labusch, K.: MaxMinOver: Ein neues iteratives Verfahren zur Supportvektor-Klassifikation mit Anwendungen in der Gesichtserkennung: , 2004
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2002

  • Labusch, K. and Polani, D.: Sensor Evolution in a Homeokinetic System: Proceedings of the Fifth German Workshop on Artificial Life, Polani, D., Kim, J. T., and Martinetz, T. (Ed.), Akademische Verlagsgesellschaft Aka, Berlin, pp. 199-208, 2002
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