Startseite
Kai Labusch
erstellt von Thomas Klähn
—
zuletzt verändert:
11.03.2010 17:02
Publications
Kai Labusch
| [1] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Soft-competitive Learning of Sparse Codes and its Application to Image Reconstruction. Neurocomputing, 74(9):1418-1428, April 2011. [ bib | .pdf ] |
| [2] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent. IEEE Transactions on Selected Topics in Signal Processing, 5(5):1048 - 1060, 2011. [ bib | .pdf ] |
| [3] | Ingrid Brænne, Kai Labusch, and Amir Madany Mamlouk. Sparse Coding for Feature Selection on Genome-wide Association Data. In Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki,Greece, September 15-18, 2010, Proceedings, volume 6352 of Lecture Notes in Computer Science, pages 337-346. Springer, 2010. [ bib | .pdf ] |
| [4] | Ingrid Brænne, Kai Labusch, Thomas Martinetz, and Amir Madany Mamlouk. Interpretive Risk Assessment on GWA Data with Sparse Linear Regression. Machine Learning Reports, pages 61-68, 2010. [ bib | .pdf ] |
| [5] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Bag of Pursuits and Neural Gas for Improved Sparse Coding. In Gilbert Saporta, editor, Proceedings of the 19th International Conference on Computational Statistics, pages 327-336. Springer, 2010. [ bib | .pdf ] |
| [6] | Kai Labusch and Thomas Martinetz. Learning Sparse Codes for Image Reconstruction. In Michel Verleysen, editor, Proceedings of the 18th European Symposium on Artificial Neural Networks, pages 241-246. D-Side Publishers, 2010. [ bib | .pdf ] |
| [7] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Sparse Coding Neural Gas: Learning of Overcomplete Data Representations. Neurocomputing, 72(7-9):1547-1555, 2009. [ bib | http | .pdf ] |
| [8] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas. In J.C. Principe and R. Miikkulainen, editors, Advances in Self-Organizing Maps - WSOM 2009, 7th International Workshop, St. Augustine, Fl, USA, June 2009, volume 5629 of Lecture Notes in Computer Science, pages 145-153. Springer, 2009. [ bib | http | .pdf ] |
| [9] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Demixing Jazz-Music: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. Neural Network World, 19(5):561-579, 2009. [ bib | http | .pdf ] |
| [10] | Thomas Martinetz, Kai Labusch, and Daniel Schneegaß. SoftDoubleMaxMinOver: Perceptron-like Training of Support Vector Machines. IEEE Transactions on Neural Networks, 20(7):1061-1072, 2009. [ bib | http | .pdf ] |
| [11] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Learning data representations with sparse coding neural gas. In Michel Verleysen, editor, Proceedings of the 16th European Symposium on Artificial Neural Networks, pages 233-238. D-Side Publishers, 2008. [ bib | http | .pdf ] |
| [12] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. In Vera Kurková, Roman Neruda, and Jan Koutník, editors, Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II, volume 5163 of Lecture Notes in Computer Science, pages 788-797. Springer, 2008. [ bib | http | .pdf ] |
| [13] | Kai Labusch, Erhardt Barth, and Thomas Martinetz. Simple Method for High-Performance Digit Recognition Based on Sparse Coding. IEEE Transactions on Neural Networks, 19(11):1985-1989, 2008. [ bib | http | .pdf ] |
| [14] | Kai Labusch, Fabian Timm, and Thomas Martinetz. Simple incremental one-class Support Vector classification. In Gerhard Rigoll, editor, Pattern Recognition - Proceedings of the DAGM, Lecture Notes in Computer Science, pages 21-30, 2008. [ bib | .pdf ] |
| [15] | Kai Labusch, Udo Siewert, Thomas Martinetz, and Erhardt Barth. Learning optimal features for visual pattern recognition. In Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly, editors, Human Vision and Electronic Imaging XII, volume 6492. Proceedings of SPIE, 2007. [ bib | .pdf ] |
| [16] | Daniel Schneegaß, Kai Labusch, and Thomas Martinetz. MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation. In Artificial Neural Networks - ICANN 2006, Lecture Notes in Computer Science, pages 150-58, Berlin/Heidelberg, 2006. Springer. [ bib | .pdf ] |
| [17] | Thomas Martinetz, Kai Labusch, and Daniel Schneegaß. SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification. In Wlodzislaw Duch, Janusz Kacprzyk, Erkki Oja, and Slawomir Zadrozny, editors, Artificial Neural Networks: Biological Inspirations. ICANN 2005: 15th International Conference. Proceedings, Part II, Lecture Notes in Computer Science, pages 301-306, 2005. [ bib | http | .pdf ] |
| [18] | Kai Labusch. MaxMinOver: Ein neues iteratives Verfahren zur Supportvektor-Klassifikation mit Anwendungen in der Gesichtserkennung. Diploma thesis, University of Lübeck, 2004. [ bib | http | .pdf ] |
| [19] | Kai Labusch and Daniel Polani. Sensor Evolution in a Homeokinetic System. In Daniel Polani, Jan T. Kim, and Thomas Martinetz, editors, Proceedings of the Fifth German Workshop on Artificial Life, pages 199-208, Berlin, 2002. Akademische Verlagsgesellschaft Aka. [ bib | .pdf ] |
This file was generated by bibtex2html 1.95.
