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Thomas Martinetz
erstellt von Thomas Klähn
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zuletzt verändert:
11.03.2010 17:03
Publikationen
Thomas Martinetz
| [1] | Foti Coleca, Sascha Klement, Thomas Martinetz, and Erhardt Barth. Real-time skeleton tracking for embedded systems. In Mobile Computational Photography, volume 8667D. Proceedings of SPIE, 2013. [ bib | .pdf ] |
| [2] | Hans G. Danielmeyer and Thomas Martinetz. The physics of business cycles and inflation. Preprint, 2013. [ bib | .pdf ] |
| [3] | Hans G. Danielmeyer and Thomas Martinetz. Predicting economic growth with classical physics and human biology. Preprint, 2013. [ bib | .pdf ] |
| [4] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Kai-Uwe Kalies, Enno Hartmann, and Thomas Martinetz. EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection. Journal of Theoretical Biology, 317:377-383, 2013. [ bib | http | .pdf ] |
| [5] | Mike Wellner, Thomas Käster, Thomas Martinetz, and Erhardt Barth. Optimizing depth-of-eld based on a range map and a wavelet transform. In Mobile Computational Photography, volume 8667D. Proceedings of SPIE, 2013. [ bib | .pdf ] |
| [6] | T. Binder, F. Kriener, C. Wichner, M. Wille, M. Wellner, T. Kaester, T. Martinetz, and E. Barth. How to make a small phone camera shoot like a big DSLR: creating and fusing multi-modal exposure series. In Human Vision and Electronic Imaging XVII, volume 8291 of Proceedings SPIE, 2012. [ bib | DOI | .pdf ] |
| [7] | Jens Hocke, Erhardt Barth, and Thomas Martinetz. Application of non-linear transform coding to image processing. In Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Huib de Ridder, editors, Human Vision and Electronic Imaging XVII, volume 8291. Proceedings of SPIE, 2012. [ bib | .pdf ] |
| [8] | Jens Hocke, Kai Labusch, Erhardt Barth, and Thomas Martinetz. Sparse coding and selected applications. KI - Künstliche Intelligenz, 26(4):349-355, 2012. [ bib | .pdf ] |
| [9] | Henry Schütze, Thomas Martinetz, Silke Anders, and Amir Madany Mamlouk. A Multivariate Approach to Estimate Complexity of FMRI Time Series. In Alessandro E.P. Villa, Wlodzislaw Duch, Péter Érdi, Francesco Masulli, and Günter Palm, editors, Artificial Neural Networks and Machine Learning - ICANN 2012, 22nd International Conference, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II, volume 7553 of Lecture Notes in Computer Science, pages 540-547. Springer, 2012. [ bib | .pdf ] |
| [10] | Eleonora Vig, Michael Dorr, Thomas Martinetz, and Erhardt Barth. Intrinsic dimensionality predicts the saliency of natural dynamic scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(6):1080-1091, 2012. [ bib | .pdf ] |
| [11] | Jörn Voigt, Christopher Krause, Edda Rohwäder, Sandra Saschenbrecker, Melanie Hahn, Maick Danckwardt, Christian Feirer, Konstantin Ens, Kai Fechner, Erhardt Barth, Thomas Martinetz, and Winfried Stöcker. Automated Indirect Immunofluorescence Evaluation of Antinuclear Autoantibodies on HEp-2 Cells. Clinical and Developmental Immunology, 2012:7 pages, 2012. [ bib | DOI | .pdf ] |
| [12] | Arne Weigenand, Thomas Martinetz, and Jens Christian Claussen. The phase response of the cortical slow oscillation. Cognitive Neurodynamics, 6:367-375, 2012. [ bib | http | http ] |
| [13] | 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 ] |
| [14] | Krishna Kumar Kandaswamy, Kuo-Chen Chou, Thomas Martinetz, Steffen Möller, Ponnuthurai Nagaratnam Suganthan, S. Sridharan, and Pugalenthi Ganesan. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. Journal of Theoretical Biology, 270:56-62, 2011. [ bib ] |
| [15] | Sascha Klement and Thomas Martinetz. On the problem of finding the least number of features by L1-norm minimisation. In T. Honkela et al., editor, ICANN 2011, Part I, LNCS 6791, pages 315-322. Springer, Heidelberg, 2011. [ bib | .pdf ] |
| [16] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Mehrnaz Khodam Hazrati, Kai-Uwe Kalies, and Thomas Martinetz. BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and Relief feature selection. BMC Bioinformatics, 2011. [ bib | .pdf ] |
| [17] | 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 ] |
| [18] | Eleonora Vig, Michael Dorr, Thomas Martinetz, and Erhardt Barth. Eye movements show optimal average anticipation with natural dynamic scenes. Cognitive Computation, 3(1):79-88, 2011. [ bib | http | .pdf ] |
| [19] | Jiajie Zhang, Amir Madany Mamlouk, Thomas Martinetz, Suhua Chang, Jing Wang, and Rolf Hilgenfeld. PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus genome. BMC Bioinformatics, 12:248, 2011. [ bib | http ] |
| [20] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. Shading constraint improves accuracy of time-of-flight measurements. Computer Vision and Image Understanding, 114:1329-1335, 2010. [ bib | .pdf ] |
| [21] | 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 ] |
| [22] | H. G. Danielmeyer and T. Martinetz. The Biologic Stability of the Industrial Evolution. European Review (Academia Europea), Vol. 18, No.2:263-268, 2010. [ bib | .pdf ] |
| [23] | Michael Dorr, Thomas Martinetz, Karl Gegenfurtner, and Erhardt Barth. Variability of eye movements when viewing dynamic natural scenes. Journal of Vision, 10(10):1-17, 2010. [ bib | http ] |
| [24] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Enno Hartmann, Kai-Uwe Kalies, Steffen Möller, P.N. Suganthan, and Thomas Martinetz. SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes. Biochemical and Biophysical Research Communications, 391:1306-1311, 2010. [ bib | .pdf ] |
| [25] | Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Steffen Möller, Enno Hartmann, Kai-Uwe Kalies, P.N.Suganthan, and Thomas Martinetz. Prediction of apoptosis protein locations with Genetic Algorithms and Support Vector Machines through a new mode of pseudo amino acid composition. Protein Peptide Letters, 17:1473-1479, 2010. [ bib ] |
| [26] | Sascha Klement and Thomas Martinetz. The support feature machine for classifying with the least number of features. In Konstantinos I. Diamantaras, Wlodek Duch, and Lazaros S. Iliadis, editors, Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part II, volume 6353 of Lecture Notes in Computer Science, pages 88-93. Springer, 2010. [ bib | .pdf ] |
| [27] | Sascha Klement and Thomas Martinetz. A new approach to classification with the least number of features. In Proceedings of the 9th International Conference on Machine Learning and Applications - ICMLA 2010, Whashington, D.C, USA, 12-14 December, 2010, pages 141-146. IEEE Computer Society, 2010. [ bib | .pdf ] |
| [28] | 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 ] |
| [29] | 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 ] |
| [30] | Ganesan Pugalenthi, Krishna Kumar Kandaswamy, P. N. Suganthan, R.Sowdhamini, Thomas Martinetz, and Prasanna Kolatkar. A support vector machine approach to identify structural motifs in protein structure without using evolutionary information. Journal of Biomolecular Structure and Dynamics, 28, 2010. [ bib | .pdf ] |
| [31] | Fabian Timm and Thomas Martinetz. Statistical Fourier descriptors for defect image classification. In Proceedings of the 20th Int. Conference on Pattern Recognition (ICPR), Istanbul, Turkey, 2010. IEEE Computer Society Press. [ bib | .pdf ] |
| [32] | Fabian Timm, Thomas Martinetz, and Erhardt Barth. Optical inspection of welding seams. In Computer Vision, Imaging and Computer Graphics: Theory and Applications, Revised Selected Papers, volume 68, pages 269-282. Springer Series Communications in Computer Science and Information Science, 2010. [ bib | .pdf ] |
| [33] | Eleonora Vig, Michael Dorr, Thomas Martinetz, and Erhardt Barth. A learned saliency predictor for dynamic natural scenes. In K. Diamantaras, W. Duch, and L. S. Iliadis, editors, ICANN 2010, Part III, volume 6354 of Lecture Notes in Computer Science, pages 52-61, Thessaloniki, Greece, 2010. Springer. [ bib | .pdf ] |
| [34] | Arne Weigenand, Hong-Viet V. Ngo, David Higgins, Thomas Martinetz, and Jens Christian Claussen. Switching between up and down states in a conductance-based cortex model. In Proceedings of the International Biosignal Processing Conference, Berlin, Germany, pages 105:1-3, 2010. [ bib ] |
| [35] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. Head tracking with combined face and nose detection. In Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), Iasi, Romania, 2009. (to appear). [ bib | .pdf ] |
| [36] | Martin Böhme, Martin Haker, Kolja Riemer, Thomas Martinetz, and Erhardt Barth. Face detection using a time-of-flight camera. In Dynamic 3D Imaging - Workshop in Conjunction with DAGM, volume 5742 of Lecture Notes in Computer Science, pages 167-176, 2009. http://www.springerlink.com/content/023881623j67r336/. [ bib | .pdf ] |
| [37] | H. G. Danielmeyer and T. Martinetz. An exact theory of the industrial evolution and national recovery. Technical Report SIIM-TR-A-09-05, version 11-09, Institut für Neuro- und Bioinformatik der Universität zu Lübeck, www.inb.uni-luebeck.de, 2009. [ bib | .pdf ] |
| [38] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Deictic gestures with a time-of-flight camera. In Stefan Kopp and Ipke Wachsmuth, editors, Gesture in Embodied Communication and Human-Computer Interaction - International Gesture Workshop GW 2009, volume 5934 of LNAI, pages 110-121. Springer, 2009. http://www.springerlink.com/content/uw12051512tk5261/. [ bib | .pdf ] |
| [39] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Self-organizing maps for pose estimation with a time-of-flight camera. In Dynamic 3D Imaging - Workshop in Conjunction with DAGM, volume 5742 of Lecture Notes in Computer Science, pages 142-153, 2009. http://www.springerlink.com/content/006305183070t383/. [ bib | .pdf ] |
| [40] | Martin Haker, Thomas Martinetz, and Erhardt Barth. Multimodal sparse features for object detection. In Artificial Neural Networks - ICANN 2009, 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, volume 5769 of Lecture Notes in Computer Science, pages 923-932. Springer, 2009. http://www.springerlink.com/content/574230078m0228wh/. [ bib | .pdf ] |
| [41] | 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 ] |
| [42] | 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 ] |
| [43] | 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 ] |
| [44] | 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 ] |
| [45] | Dirk Repsilber, Thomas Martinetz, and Mats Björklund. Adaptive Dynamics of Regulatory Networks: Size Matters. EURASIP Journal on Bioinformatics and Systems Biology, Volume 2009 (2009), Article ID 618502, 10 pages, doi:10.1155/2009/618502, 2009. [ bib | .pdf ] |
| [46] | Fabian Timm, Sascha Klement, Thomas Martinetz, and Erhardt Barth. Welding inspection using novel specularity features and a one-class SVM. In Proceedings of the Int. Conference on Computer Theory and Applications (VISAPP), volume 1, pages 146-153, Lisboa, Portugal, 2009. INSTICC. [ bib | .pdf ] |
| [47] | Martin Böhme, Michael Dorr, Mathis Graw, Thomas Martinetz, and Erhardt Barth. A software framework for simulating eye trackers. In Proceedings of Eye Tracking Research & Applications (ETRA), pages 251-258, 2008. (c) ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Eye Tracking Research & Applications 2008, Savannah, Georgia, 26-28 March 2008. http://doi.acm.org/10.1145/1344471.1344529. [ bib | .pdf ] |
| [48] | Martin Böhme, Michael Dorr, Thomas Martinetz, and Erhardt Barth. A temporal multiresolution pyramid for gaze-contingent manipulation of natural video. In Riad I. Hammoud, editor, Passive Eye Monitoring, chapter 10, pages 225-243. Springer, 2008. [ bib ] |
| [49] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. A facial feature tracker for human-computer interaction based on 3D Time-of-Flight cameras. International Journal of Intelligent Systems Technologies and Applications, 5(3/4):264-273, 2008. [ bib | .pdf ] |
| [50] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. Shading constraint improves accuracy of time-of-flight measurements. In CVPR 2008 Workshop on Time-of-Flight-based Computer Vision (TOF-CV), 2008. [ bib | .pdf ] |
| [51] | Michael Dorr, Eleonora Vig, Karl R Gegenfurtner, Thomas Martinetz, and Erhardt Barth. Eye movement modelling and gaze guidance. In Fourth International Workshop on Human-Computer Conversation, 2008. [ bib | .pdf ] |
| [52] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Scale-invariant range features for time-of-flight camera applications. In CVPR 2008 Workshop on Time-of-Flight-based Computer Vision (TOF-CV), 2008. [ bib | .pdf ] |
| [53] | Sascha Klement, Amir Madany Mamlouk, and Thomas Martinetz. Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios. 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 41-50. Springer, 2008. http://www.springerlink.com/content/a852001534335865/. [ bib | .pdf ] |
| [54] | 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 ] |
| [55] | 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 ] |
| [56] | 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 ] |
| [57] | 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 ] |
| [58] | Daniel Schneegaß, Steffen Udluft, and Thomas Martinetz. Uncertainty Propagation for Quality Assurance in Reinforcement Learning. In Proc. of the International Joint Conference on Neural Networks, pages 2589-2596, 2008. [ bib | .pdf ] |
| [59] | Fabian Timm, Sascha Klement, and Thomas Martinetz. Fast model selection for MaxMinOver-based training of Support Vector Machines. In Proceedings of the 19th Int. Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008. IEEE Computer Society Press. [ bib | .pdf ] |
| [60] | Michael Dorr, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Gaze beats mouse: a case study. In The 3rd Conference on Communication by Gaze Interaction - COGAIN 2007, Leicester, UK, pages 16-19, 2007. [ bib | .pdf ] |
| [61] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Geometric invariants for facial feature tracking with 3D TOF cameras. In Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), volume 1, pages 109-112, Iasi, Romania, 2007. [ bib | .pdf ] |
| [62] | 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 ] |
| [63] | Daniel Schneegaß, Anton Maximilian Schaefer, and Thomas Martinetz. The Intrinsic Recurrent Support Vector Machine. In Michel Verleysen, editor, Proc. of the European Symposium on Artificial Neural Networks, pages 325-330, 2007. [ bib | .pdf ] |
| [64] | Daniel Schneegaß, Steffen Udluft, and Thomas Martinetz. Explicit Kernel Rewards Regression for Data-Efficient Near-optimal Policy Identification. In Michel Verleysen, editor, Proc. of the European Symposium on Artificial Neural Networks, pages 337-342, 2007. [ bib | .pdf ] |
| [65] | Daniel Schneegaß, Steffen Udluft, and Thomas Martinetz. Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. In Proc. of the International Conference on Artificial Neural Networks, pages 109-118, 2007. [ bib | .pdf ] |
| [66] | Daniel Schneegaß, Steffen Udluft, and Thomas Martinetz. Neural Rewards Regression for Near-Optimal Policy Identification in Markovian and Partial Environments. In Michel Verleysen, editor, Proc. of the European Symposium on Artificial Neural Networks, pages 301-306, 2007. [ bib | .pdf ] |
| [67] | Erhardt Barth, Michael Dorr, Martin Böhme, Karl R. Gegenfurtner, and Thomas Martinetz. Guiding the mind's eye: improving communication and vision by external control of the scanpath. In Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly, editors, Human Vision and Electronic Imaging, volume 6057 of Proc. SPIE, 2006. Invited contribution for a special session on Eye Movements, Visual Search, and Attention: a Tribute to Larry Stark. [ bib | .pdf ] |
| [68] | Erhardt Barth, Michael Dorr, Martin Böhme, Karl R Gegenfurtner, and Thomas Martinetz. Guiding eye movements for better communication and augmented vision. In Perception and Interactive Technologies, volume 4021 of Lecture Notes in Artificial Intelligence, pages 1-8. Springer, 2006. [ bib | .pdf ] |
| [69] | Martin Böhme, Michael Dorr, Christopher Krause, Thomas Martinetz, and Erhardt Barth. Eye movement predictions on natural videos. Neurocomputing, 69(16-18):1996-2004, 2006. [ bib | .pdf ] |
| [70] | Martin Böhme, Michael Dorr, Thomas Martinetz, and Erhardt Barth. Gaze-contingent temporal filtering of video. In Proceedings of Eye Tracking Research & Applications (ETRA), pages 109-115, 2006. (c) ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Eye Tracking Research & Applications 2006, San Diego, California, 27-29 March 2006. http://doi.acm.org/10.1145/1117309.1117353. [ bib | .pdf ] |
| [71] | Martin Böhme, André Meyer, Thomas Martinetz, and Erhardt Barth. Remote eye tracking: State of the art and directions for future development. In The 2nd Conference on Communication by Gaze Interaction - COGAIN 2006, Turin, Italy, pages 10-15, 2006. [ bib | .pdf ] |
| [72] | Michael Dorr, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Gaze-contingent spatio-temporal filtering in a head-mounted display. In Perception and Interactive Technologies, volume 4021 of Lecture Notes in Artificial Intelligence, pages 205-207. Springer, 2006. [ bib | .pdf ] |
| [73] | T. Martinetz, A. Madany Mamlouk, and C. Mota. Fast and Easy Computation of Approximate Smallest Enclosing Balls. Proc. SIBGRAPI, pages 163-170, 2006. [ bib | .pdf ] |
| [74] | André Meyer, Martin Böhme, Thomas Martinetz, and Erhardt Barth. A single-camera remote eye tracker. In Perception and Interactive Technologies, volume 4021 of Lecture Notes in Artificial Intelligence, pages 208-211. Springer, 2006. [ bib | .pdf ] |
| [75] | Daniel Polani, Chrystopher Nehaniv, Thomas Martinetz, and Jan T. Kim. Relevant Information in Optimized Persistence vs. Progeny Strategies. In L. M.Rocha, M. Bedau, D. Floreano, R. Goldstone, A. Vespignani, and L. Yaeger, editors, Proc. Artificial Life X, 2006. [ bib | .pdf ] |
| [76] | 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 ] |
| [77] | Daniel Schneegaß, Steffen Udluft, and Thomas Martinetz. Kernel rewards regression: An information efficient batch policy iteration approach. In V. Devedzic, editor, Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, pages 428-433, 2006. [ bib | .pdf ] |
| [78] | U. Brinkschulte, J. Becker, D. Fey, C. Hochberger, T. Martinetz, C. Müller-Schloer, H. Schmeck, T. Ungerer, and R. Würtz. Arcs 2005 - system aspects in organic and pervasive computing - workshops procedeedings, innsbruck austria, march 14-17, 2005. [ bib ] |
| [79] | Michael Dorr, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Visibility of temporal blur on a gaze-contingent display. In APGV 2005 ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization, pages 33-36, 2005. [ bib | .pdf ] |
| [80] | Michael Dorr, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Predicting, analysing, and guiding eye movements. In Neural Information Processing Systems Conference (NIPS 2005), Workshop on Machine Learning for Implicit Feedback and User Modeling, 2005. [ bib | .pdf ] |
| [81] | 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 ] |
| [82] | A. Madany Mamlouk, H. Sharp, K. M. L. Menne, U. G. Hofmann, and T. Martinetz. Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations. Neurocomputing, 65-66:275-282, 2005. [ bib | .pdf ] |
| [83] | Martin Böhme, Christopher Krause, Erhardt Barth, and Thomas Martinetz. Eye Movement Predictions Enhanced by Saccade Detection. In Brain Inspired Cognitive Systems, page CD, 2004. [ bib | .pdf ] |
| [84] | Martin Böhme, Christopher Krause, Thomas Martinetz, and Erhardt Barth. Saliency Extraction for Gaze-Contingent Displays. In Proceedings of the 34th GI-Jahrestagung, volume 2, pages 646-650, 2004. Workshop on Organic Computing. [ bib | .pdf ] |
| [85] | Michael Dorr, Thomas Martinetz, Karl Gegenfurtner, and Erhardt Barth. Guidance of eye movements on a gaze-contingent display. In Uwe J. Ilg, Heinrich H. Bülthoff, and Hanspeter A. Mallot, editors, Dynamic Perception Workshop of the GI Section “Computer Vision”, pages 89-94, 2004. [ bib | .pdf ] |
| [86] | J. T. Kim, J. Gewehr, and T. Martinetz. Binding matrix: A novel approach for binding site recognition. Journal of Bioinformatics and Computational Biology, 2:289-307, 2004. [ bib | .pdf ] |
| [87] | J. T. Kim, T. Martinetz, and D. Polani. On the Evolution of Information in the Constituents of Regulatory Gene. In A. Deutsch, J. Howard, M. Falcke, and W. Zimmermann, editors, Function and Regulation of Cellular Systems. Experiments and Models, pages 259-264, Basel, 2004. [ bib ] |
| [88] | A. Madany Mamlouk and T. Martinetz. On the Dimensions of the Olfactory Perception Space. Neurocomputing, 58-60:1019-1025, 2004. [ bib | .pdf ] |
| [89] | T. Martinetz and A. Madany Mamlouk. Easy and Fast Computation of Approximate Smallest Enclosing Balls. Technical Report SIIM-TR-A-04-13, University of Lübeck, 2004. [ bib | .pdf ] |
| [90] | T. Martinetz. MinOver Revisited for Incremental Support-Vector-Classification. In C.E. Rasmussen, H.H. Buelthoff, M. Giese, and B. Schoelkopf, editors, DAGM 2004, volume 3175 of LNCS, pages 187-194. Springer-Verlag Berlin Heidelberg, 2004. [ bib | .pdf ] |
| [91] | Thomas Martinetz. MaxMinOver: A Simple Incremental Learning Procedure for Support Vector Classification. In IEEE Proceedings of the International Joint Conference on Neural Networks (IJCNN 2004), pages 2065-2070, Budapest, Hungary, 2004. [ bib | .pdf ] |
| [92] | Erhardt Barth, Jan Drewes, and Thomas Martinetz. Individual predictions of eye-movements with dynamic scenes. In Bernice E Rogowitz and Thrasyvoulos N Pappas, editors, Electronic Imaging 2003, volume 5007, pages 252-259. SPIE, 2003. [ bib | .html ] |
| [93] | E Barth, J Drewes, and T Martinetz. Dynamic predictions of tracked gaze. In Seventh International Symposium on Signal Processing and its Applications, Paris, 2003. Special Session on Foveated Vision in Image and Video Processing. [ bib | .pdf ] |
| [94] | J. T. Kim, T. Martinetz, and D. Polani. Bioinformatic Principles Underlying the Information Content of Transcription Factor Binding Sites. Journal of Theoretical Biology, 220:529-544, 2003. [ bib | .pdf ] |
| [95] | T. Martinetz, J. Gewehr, and J. T. Kim. Statistical Learning for Detecting Protein-DNA-Binding Sites. In D. C. Wunsch II, M. Hasselmo, and K. Venayagamoorthy, editors, Proceedings of the International Joint Conference on Neural Networks 2003, pages 2940-2945. IEEE Press, 2003. [ bib | .pdf ] |
| [96] | A. Madany Mamlouk, J. T. Kim, E. Barth, M. Brauckmann, and T. Martinetz. One-Class Classification with Subgaussians. In B. Michaelis and G. Krell, editors, Pattern Recognition (DAGM 2003), volume 2781 of LNCS, pages 346-353. Springer-Verlag Berlin Heidelberg, 2003. [ bib | .pdf ] |
| [97] | A. Meyer-Bäse, T. Otto, T. Martinetz, D. Auer, and A. Wismüller. Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN), volume D-Side Publishers 4, pages 509-514, 2003. [ bib | .pdf ] |
| [98] | E. Barth and T. Martinetz. Information technology for active perception. In 8th Annual German-American Beckman Frontiers of Science Symposium, 2002. Poster in PDF. [ bib | .pdf ] |
| [99] | Dirk Repsilber, Jan T. Kim, Hans Liljenström, and Thomas Martinetz. Using Coarse-Grained, Discrete Systems for Data-Driven Inference of Regulatory Gene Networks: Perspectives and Limitations for Reverse Engineering. In Daniel Polani, Jan T. Kim, and Thomas Martinetz, editors, Proceedings of the Fifth German Workshop on Artificial Life, pages 67-76, Berlin, 2002. infix / Akademische Verlagsgesellschaft Aka. [ bib | .pdf ] |
| [100] | M. Haker, A. Meyer, D. Polani, and T. Martinetz. A Method for Incorporation of New Evidence to Improve World State Estimation. In Proceedings of the RoboCup 2001 Symposium, Seattle, pages 356-361, 2001. [ bib | .pdf ] |
| [101] | Jan T. Kim, Thomas Martinetz, and Daniel Polani. On the Effects of Transcription Factor Properties on the Information Content of Binding Sites. In Edgar Wingender, Ralf Hofestädt, and Ines Liebich, editors, Proceedings of the German Conference on Bioinformatics 2001. German Research Center for Biotechnology (GBF), Maschroder Weg 1, 38124 Braunschweig, Germany, pages 192-194, 2001. [ bib | .pdf ] |
| [102] | T. Martinetz. Bioinformatik - Informationsverarbeitung in der Biologie. Focus MUL, 18(2):74-81, 2001. [ bib | .pdf ] |
| [103] | D. Polani and T. Martinetz. Team Description for Lucky Lübeck - Evidence Based Position Estimation. In P. Stone, T. Balch, and G. Kraetzschmar, editors, RoboCup-2000. Robot Soccer World Cup IV, LNCS, pages 481-484. Springer, 2001. [ bib | .ps.gz ] |
| [104] | D. Polani, T. Martinetz, and J. T. Kim. An Information-Theoretic Approach for the Quantification of Relevance. In J. Kelemen and P. Sosik, editors, Advances in Artificial Life (Proc. 6th European Conference on Artificial Life), LNCS. Springer-Verlag, 2001. [ bib | .pdf ] |
| [105] | D. Polani, T. Martinetz, and J. T. Kim. On the Quantification of Relevant Information, 2001. Presented at SCAI'01 (Scandinavian Conference on Artificial Intelligence), Feb. 19-21, 2001. [ bib | .ps.gz ] |
| [106] | C. Ronnewinkel and T. Martinetz. Explicit Speciation with few a priori Parameters for Dynamic Optimization Problems. In GECCO 2001 - Workshop Proceedings, San Francisco, 2001. Morgan Kaufmann. [ bib | .ps.gz ] |
| [107] | C. Ronnewinkel, C.O. Wilke, and T. Martinetz. Genetic algorithms in time-dependent environments. L. Kallel, B. Naudts, and A. Rogers, editors, Theoretical Aspects of Evolutionary Computing, Natural Computing, pages 261-285, 2001. [ bib | .pdf ] |
| [108] | Claus O Wilke, Christopher Ronnewinkel, and Thomas Martinetz. Dynamic fitness landscapes in molecular evolution. Physics Reports, 349:395-446, 2001. [ bib | .pdf ] |
| [109] | C.O. Wilke and T. Martinetz. Lifetimes of agents under external stress. Phys. Rev. E, Rapid Communication, 59(3):R2512-R2515, 1999. [ bib | .pdf ] |
| [110] | Claus O Wilke and Thomas Martinetz. Adaptive walks on time-dependent fitness landscapes. Physical Review E, 60(2):2154-2159, 1999. [ bib | .pdf ] |
| [111] | Claus O. Wilke, Christopher Ronnewinkel, and Thomas Martinetz. Molecular evolution in time dependent environments. In Dario Floreano, Jean-Daniel Nicoud, and Francesco Mondada, editors, Advances in Artificial Life, ECAL 1999, Lecture Notes in Artifical Intelligence. Springer, 1999. [ bib | .pdf ] |
| [112] | S. Altmeyer, C. O. Wilke, and T. Martinetz. How fast do structures emerge in hypercycle systems? In C. O. Wilke, S. Altmeyer, and T. Martinetz, editors, Third German Workshop on Artificial Life. Verlag Harri Deutsch, 1998. [ bib | .pdf ] |
| [113] | Claus O Wilke, Stephan Altmeyer, and Thomas Martinetz. Large-scale evolution and extinction in a hierarchically structured environment. In C. Adami, R. Belew, H. Kitano, and C. Taylor, editors, Proceedings of Artificial Life VI, Los Angeles, June 26-29, 1998. MIT Press, 1998. [ bib | .pdf ] |
| [114] | C. O. Wilke, S. Altmeyer, and T. Martinetz. Aftershocks in Coherent-Noise Models. Physica D, 120:401-417, 1998. [ bib | .pdf ] |
| [115] | C. O. Wilke and T. Martinetz. A coarse-grained model of evolution with variable system size. In P. Dittrich, H. Rauhe, and W. Banzhaf, editors, 2nd German Workshop on Artificial Life, Dortmund 1998 = SYS Report, Internal Report of Systems Analysis Research Group, University of Dortmund, Department of Computer Science, Dortmund, Germany, 1998. [ bib | .ps.gz ] |
| [116] | C.O. Wilke and T. Martinetz. Hierarchical noise in large systems of independent agents. Phys. Rev. E, 58(6):7101-7108, 1998. [ bib | .pdf ] |
| [117] | T. Villmann, R. Der, M. Herrmann, and T. Martinetz. Topology Preservation in Self-Organizing Feature Maps: Exact Definition and Measurement. IEEE-Transactions on Neural Networks, 8(2):256-266, 1997. [ bib | .pdf ] |
| [118] | C. O. Wilke and T. Martinetz. A Simple Model of Evolution with Variable System Size. Phys. Rev. E, 56:7128-7131, 1997. [ bib | .pdf ] |
| [119] | H. G. Danielmeyer and T. Martinetz. Best practice code of the industrial society - the Forum Engelberg model. European Review (Academia Europea), 1996. [ bib ] |
| [120] | H. G. Danielmeyer and T. Martinetz. Innovation, investment, and sustainable growth. In Acta 7. Forum Engelberg, 1996. [ bib ] |
| [121] | T. Martinetz, O. Gramckow, P. Protzel, and G. Sörgel. Neuronale Netze zur Steuerung von Walzstraßen. atp - Automatisierungstechnische Praxis, 10/96, 1996. [ bib | .pdf ] |
| [122] | T. Martinetz and J. Hollatz. Neuro-Fuzzy in der Prozeßautomatisierung. In K. W. Bonfig, editor, Neuro-Fuzzy in der industriellen Automatisierung, pages 135-144, Renningen, 1995. expert-Verlag. [ bib | .pdf ] |
| [123] | T. Martinetz, P. Protzel, and O. Gramckow. Walzwerksteuerung mit neuronalen Netzen. In Neuronale Netze: Anwendungen in der Automatisierungstechnik, Bericht 1184, pages 35-42, Düsseldorf, 1995. VDI-Verlag. Auch im Jahrbuch 1997 der VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik, pages 333-340, Düsseldorf 1995. VDI-Verlag. [ bib | .pdf ] |
| [124] | T. Martinetz, P. Protzel, O. Gramckow, and G. Sörgel. Neural Network Control for Steel Rolling Mills. In B. Kappen and S. Gielen, editors, Neural Networks: Artificial Intelligence and Industrial Applications, pages 280-286, Heidelberg, 1995. Springer. [ bib | .pdf ] |
| [125] | T. Martinetz. Lernverfahren für komplexe Aktuatorik/Robotik. In H. J. Zimmermann, editor, Neuro-Fuzzy Technologien - Anwendungen, pages 89-101, Düsseldorf, 1995. VDI-Verlag. [ bib | .pdf ] |
| [126] | G. Deco and T. Martinetz. Regularizing stochastic Pott neural networks by penalizing mutual information. In M. Marinaro and P. Morasso, editors, Proceedings of the International Conference on Artificial Neural Networks (ICANN-94), Sorrent, pages 693-696, Heidelberg, 1994. Springer. [ bib | .pdf ] |
| [127] | T. Martinetz and T. Poppe. A neural network approach to estimating material properties. In Proceedings of the World Congress on Neural Networks (WCNN-94), San Diego, pages 541-544, 1994. [ bib | .pdf ] |
| [128] | T. Martinetz, P. Protzel, O. Gramckow, and G. Sörgel. Neural network control for rolling mills. In Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing (EUFIT-94), Aachen, volume I, pages 147-152, 1994. [ bib | .pdf ] |
| [129] | T. Martinetz and K. Schulten. Topology representing networks. Neural Networks, 7(3):507-522, 1994. [ bib | .pdf ] |
| [130] | T. Martinetz. Neural learning can form structures from computational geometry. In Tagungsband des 39. internationalen wissenschaftlichen Kolloquiums, Ilmenau, volume II, pages 206-211, 1994. [ bib | .pdf ] |
| [131] | T. Poppe, T. Martinetz, and K. Hohendahl. Optimierte Steuerung eines Elektrolichtbogenofens. In M. Schlang, B. Schürmann, and E. Linzenkirchner, editors, Neue Techniken der Informationsverarbeitung, pages 19-29, München, 1994. Winkler-Verlag. [ bib | .pdf ] |
| [132] | T. Villmann, R. Der, M. Herrmann, and T. Martinetz. Topology preservation in self-organizing feature maps: general definition and efficient measurement. In Lecture Notes in Computer Science, pages 159-166, Heidelberg, 1994. Springer. [ bib | .pdf ] |
| [133] | T. Villmann, R. Der, and T. Martinetz. A new quantitative measure of topology preservation in Kohonen's feature maps. In Proceedings of the IEEE International Conference on Neural Networks (ICNN-94), Orlando, volume II, pages 645-648, 1994. [ bib | .pdf ] |
| [134] | T. Villmann, R. Der, and T. Martinetz. A novel approach to measure the toplogy preservation of feature maps. In M. Marinaro and P. Morasso, editors, Proceedings of the International Conference on Artificial Neural Networks (ICANN-94), Sorrent, pages 298-301, Heidelberg, 1994. Springer. [ bib | .pdf ] |
| [135] | T. Martinetz, S. Berkovich, and K. Schulten. "Neural-gas" Network for Vector Quantization and its Application to Time-Series Prediction. IEEE-Transactions on Neural Networks, 4(4):558-569, 1993. [ bib | .pdf ] |
| [136] | T. Martinetz and K. Schulten. A neural network for robot control: cooperation between neural units as a requirement for learning. Computers & Electrical Engineering, 19(4):315-332, 1993. [ bib | .pdf ] |
| [137] | T. Martinetz and K. Schulten. A Neural Network with Hebbian-like Adaption Rules Learning Visuomotor Coordination of a PUMA Robot. In Proceedings of the IEEE International Conference on Neural Networks (ICNN-93), San Francisco, volume II, pages 820-825, 1993. [ bib | .pdf ] |
| [138] | T. Martinetz. Competitive hebbian learning rule forms perfectly topology preserving maps. In S. Gielen and B. Kappen, editors, Proceedings of the International Conference on Artificial Neural Networks (ICANN-93), Amsterdam, pages 427-434, Heidelberg, 1993. Springer. [ bib | .pdf ] |
| [139] | T. Martinetz. Neuronale Karten in der Robotik: Lernen visuell geführter Greifbewegungen. In Konferenzband des 3. Anwendersymposiums zu Neuro-Fuzzy Technologien, Bochum, 1993. [ bib | .pdf ] |
| [140] | T. Poppe and T. Martinetz. Estimating material properties for process optimization. In S. Gielen and B. Kappen, editors, Proceedings of the International Conference on Artificial Neural Networks (ICANN-93), Amsterdam, pages 795-798, Heidelberg, 1993. Springer. [ bib | .pdf ] |
| [141] | T. Martinetz. Selbstorganisierende neuronal Netzwerkmodelle zur Bewegungssteuerung, 1992. Dissertationen zur künstlichen Intelligenz. [ bib ] |
| [142] | H. Ritter, T. Martinetz, and K. Schulten. Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley, Massachusetts, 1992. [ bib | .pdf ] |
| [143] | S. Berkovich, P. Dalger, T. Hesselroth, T. Martinetz, B. Noël, J. Walter, and K. Schulten. Vector quantization algorithm for time-series prediction and visuo-motor control of robots. In H. W. Brauer, editor, Verteilte Künstliche Intelligenz und Kooperatives Arbeiten, pages 443-447, Heidelberg, 1991. [ bib ] |
| [144] | T. Martinetz and K. Schulten. A "Neural-Gas" Network Learns Topologies. Artificial Neural Networks, I:397-402, 1991. [ bib | .pdf ] |
| [145] | H. Ritter, T. Martinetz, and K. Schulten. Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke. Addison-Wesley, Bonn, 2 edition, 1991. [ bib ] |
| [146] | J. Walter, T. Martinetz, and K. Schulten. Industrial Robot learns Visuo-motor Coordination by Means of "Neural-Gas" Network. In T. Kohonen et al., editor, Artificial Neural Networks, volume I, pages 357-364, Amsterdam, 1991. [ bib | .pdf ] |
| [147] | T. Martinetz, H. Ritter, and K. Schulten. Learning of Visuomotor-Coordination of a Robot Arm with Redundant Degrees of Freedom. In R. Eckmiller, G. Hartmann, and G. Hauske, editors, Proceedings of the International Conference on Parallel Processing in Neural Sytems and Computers (ICNC-90), Düsseldorf 1990, pages 431-434, Amsterdam, 1990. And in: Proceedings of the Third International Symposium on Robotics and Manufacturing, Vancouver 1990 (ISRAM-90), pages 521-526, 1990. [ bib | .pdf ] |
| [148] | T. Martinetz, H. Ritter, and K. Schulten. Three-Dimensional Neural Net for Learning Visuomotor Coordination of a Robot Arm. IEEE-Transactions on Neural Networks, 1(1):131-136, 1990. [ bib | .pdf ] |
| [149] | T. Martinetz and K. Schulten. Hierarchical Neural Net for Learning Control of a Robot's Arm and Gripper. In Proceedings of the International Joint Conference on Neural Networks (IJCNN-90), San Diego 1990, volume II, pages 747-752, 1990. [ bib | .pdf ] |
| [150] | H. Ritter, T. Martinetz, and K. Schulten. Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke. Addison-Wesley, Bonn, 1990. [ bib ] |
| [151] | T. Martinetz, H. Ritter, and K. Schulten. 3D-Neural-Net for Learning Visuomotor-Coordination of a Robot Arm. In Proceedings of the International Joint Conference on Neural Networks (IJCNN-89), Washington 1989, volume II, pages 351-356, 1989. [ bib | .pdf ] |
| [152] | T. Martinetz, H. Ritter, and K. Schulten. Kohonen's Self-organizing Map for Modeling the Formation of the Auditory Cortex of a Bat. In R. Pfeifer, Z. Schreter, F. Fogelman-Soulie, and L. Steels, editors, Connectionism in Perspective, pages 403-412, Amsterdam, 1989. [ bib | .pdf ] |
| [153] | H. Ritter, T. Martinetz, and K. Schulten. Ein Gehirn für Roboter - Wie neuronale Netzwerke Roboter steuern können. MC-Computermagazin, 2:48-61, 1989. [ bib | .pdf ] |
| [154] | H. Ritter, T. Martinetz, and K. Schulten. Topology-Conserving Maps for Learning Visuo-Motor-Coordination. Neural Networks, 2:159-168, 1989. [ bib | .pdf ] |
| [155] | H. Ritter, T. Martinetz, and K. Schulten. Topology-Conserving Maps for Motor Control. In L. Personnaz and G. Dreyfus, editors, Neural Networks, from Models to Applications, pages 579-591. EZIDET, 1989. [ bib | .pdf ] |
| [156] | T. Martinetz. Selbstorganisierte visuo-motorische Kopplung. Master's thesis, Technical University of Munich, 1988. Diploma Thesis. [ bib ] |
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