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Thomas Martinetz
2010 – today
- 2013
[j23]Sascha Klement, Silke Anders, Thomas Martinetz: The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data. Neural Computation 25(6): 1548-1584 (2013)- [c47]Jens Hocke, Thomas Martinetz: Feature Weighting by Maximum Distance Minimization. ICANN 2013: 420-425
- 2012
- [j22]Thomas Binder, Thomas Martinetz: On the boundedness of an Iteration involving Points on the Hypersphere. Int. J. Comput. Geometry Appl. 22(6): 499-516 (2012)
- [j21]Jens Hocke, Kai Labusch, Erhardt Barth, Thomas Martinetz: Sparse Coding and Selected Applications. KI 26(4): 349-355 (2012)
- [j20]Eleonora Vig, Michael Dorr, Thomas Martinetz, Erhardt Barth: Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes. IEEE Trans. Pattern Anal. Mach. Intell. 34(6): 1080-1091 (2012)
- [c46]Henry Schütze, Thomas Martinetz, Silke Anders, Amir Madany Mamlouk: A Multivariate Approach to Estimate Complexity of FMRI Time Series. ICANN (2) 2012: 540-547
- 2011
- [j19]Jiajie Zhang, Amir Madany Mamlouk, Thomas Martinetz, Suhua Chang, Jing Wang, 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)
- [j18]Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Mehrnaz Khodam Hazrati, Kai-Uwe Kalies, Thomas Martinetz: BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and ReliefF feature selection. BMC Bioinformatics 12: 345 (2011)
- [j17]Eleonora Vig, Michael Dorr, Thomas Martinetz, Erhardt Barth: Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes. Cognitive Computation 3(1): 79-88 (2011)
- [j16]John A. Lee, Frank-Michael Schleif, Thomas Martinetz: Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing 74(9): 1299-1300 (2011)
- [j15]Kai Labusch, Erhardt Barth, Thomas Martinetz: Soft-competitive learning of sparse codes and its application to image reconstruction. Neurocomputing 74(9): 1418-1428 (2011)
- [j14]Kai Labusch, Erhardt Barth, Thomas Martinetz: Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent. J. Sel. Topics Signal Processing 5(5): 1048-1060 (2011)
- [c45]Sascha Klement, Thomas Martinetz: On the Problem of Finding the Least Number of Features by L1-Norm Minimisation. ICANN (1) 2011: 315-322
- 2010
- [j13]Martin Böhme, Martin Haker, Thomas Martinetz, Erhardt Barth: Shading constraint improves accuracy of time-of-flight measurements. Computer Vision and Image Understanding 114(12): 1329-1335 (2010)
- [c44]
- [c43]Eleonora Vig, Michael Dorr, Thomas Martinetz, Erhardt Barth: A Learned Saliency Predictor for Dynamic Natural Scenes. ICANN (3) 2010: 52-61
- [c42]Sascha Klement, Thomas Martinetz: The Support Feature Machine for Classifying with the Least Number of Features. ICANN (2) 2010: 88-93
- [c41]Sascha Klement, Thomas Martinetz: A New Approach to Classification with the Least Number of Features. ICMLA 2010: 141-146
- [c40]Fabian Timm, Thomas Martinetz: Statistical Fourier Descriptors for Defect Image Classification. ICPR 2010: 4190-4193
- [i2]Thomas Binder, Thomas Martinetz: On the boundedness of an iteration involving points on the hypersphere. CoRR abs/1001.1624 (2010)
2000 – 2009
- 2009
- [j12]Dirk Repsilber, Thomas Martinetz, Mats Björklund: Adaptive Dynamics of Regulatory Networks: Size Matters. EURASIP J. Bioinformatics and Systems Biology 2009 (2009)
- [j11]Kai Labusch, Erhardt Barth, Thomas Martinetz: Sparse Coding Neural Gas: Learning of overcomplete data representations. Neurocomputing 72(7-9): 1547-1555 (2009)
- [j10]Thomas Martinetz, Kai Labusch, Daniel Schneegaß: SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines. IEEE Transactions on Neural Networks 20(7): 1061-1072 (2009)
- [c39]Martin Haker, Martin Böhme, Thomas Martinetz, Erhardt Barth: Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera. Dyn3D 2009: 142-153
- [c38]Martin Böhme, Martin Haker, Kolja Riemer, Thomas Martinetz, Erhardt Barth: Face Detection Using a Time-of-Flight Camera. Dyn3D 2009: 167-176
- [c37]Martin Haker, Martin Böhme, Thomas Martinetz, Erhardt Barth: Deictic Gestures with a Time-of-Flight Camera. Gesture Workshop 2009: 110-121
- [c36]Martin Haker, Thomas Martinetz, Erhardt Barth: Multimodal Sparse Features for Object Detection. ICANN (2) 2009: 923-932
- [c35]Fabian Timm, Sascha Klement, Thomas Martinetz, Erhardt Barth: Welding Inspection using Novel Specularity Features and a One-class SVM. VISAPP (1) 2009: 146-153
- [c34]Fabian Timm, Thomas Martinetz, Erhardt Barth: Optical Inspection of Welding Seams. VISIGRAPP (Selected Papers) 2009: 269-282
- [c33]Kai Labusch, Erhardt Barth, Thomas Martinetz: Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas. WSOM 2009: 145-153
- 2008
- [j9]Martin Böhme, Martin Haker, Thomas Martinetz, Erhardt Barth: A facial feature tracker for human-computer interaction based on 3D Time-Of-Flight cameras. IJISTA 5(3/4): 264-273 (2008)
- [j8]Kai Labusch, Erhardt Barth, Thomas Martinetz: Simple Method for High-Performance Digit Recognition Based on Sparse Coding. IEEE Transactions on Neural Networks 19(11): 1985-1989 (2008)
- [c32]Kai Labusch, Fabian Timm, Thomas Martinetz: Simple Incremental One-Class Support Vector Classification. DAGM-Symposium 2008: 21-30
- [c31]Kai Labusch, Erhardt Barth, Thomas Martinetz: Learning Data Representations with Sparse Coding Neural Gas. ESANN 2008: 233-238
- [c30]Martin Böhme, Michael Dorr, Mathis Graw, Thomas Martinetz, Erhardt Barth: A software framework for simulating eye trackers. ETRA 2008: 251-258
- [c29]Sascha Klement, Amir Madany Mamlouk, Thomas Martinetz: Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios. ICANN (1) 2008: 41-50
- [c28]Kai Labusch, Erhardt Barth, Thomas Martinetz: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. ICANN (1) 2008: 788-797
- [c27]Fabian Timm, Sascha Klement, Thomas Martinetz: Fast model selection for MaxMinOver-based training of support vector machines. ICPR 2008: 1-4
- [c26]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz: Uncertainty propagation for quality assurance in Reinforcement Learning. IJCNN 2008: 2588-2595
- 2007
- [c25]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz: Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments. ESANN 2007: 301-306
- [c24]Daniel Schneegaß, Anton Maximilian Schäfer, Thomas Martinetz: The Intrinsic Recurrent Support Vector Machine. ESANN 2007: 325-330
- [c23]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz: Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification. ESANN 2007: 337-342
- [c22]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz: Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. ICANN (1) 2007: 109-118
- 2006
- [j7]Martin Böhme, Michael Dorr, Christopher Krause, Thomas Martinetz, Erhardt Barth: Eye movement predictions on natural videos. Neurocomputing 69(16-18): 1996-2004 (2006)
- [c21]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz: Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach. Artificial Intelligence and Applications 2006: 428-433
- [c20]Daniel Schneegaß, Thomas Martinetz, Michael Clausohm: OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method. ESANN 2006: 575-580
- [c19]Martin Böhme, Michael Dorr, Thomas Martinetz, Erhardt Barth: Gaze-contingent temporal filtering of video. ETRA 2006: 109-115
- [c18]Daniel Schneegaß, Kai Labusch, Thomas Martinetz: MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation. ICANN (1) 2006: 150-158
- [c17]Erhardt Barth, Michael Dorr, Martin Böhme, Karl R. Gegenfurtner, Thomas Martinetz: Guiding Eye Movements for Better Communication and Augmented Vision. PIT 2006: 1-8
- [c16]Michael Dorr, Martin Böhme, Thomas Martinetz, Erhardt Barth: Gaze-Contingent Spatio-temporal Filtering in a Head-Mounted Display. PIT 2006: 205-207
- [c15]André Meyer, Martin Böhme, Thomas Martinetz, Erhardt Barth: A Single-Camera Remote Eye Tracker. PIT 2006: 208-211
- [c14]Thomas Martinetz, Amir Madany Mamlouk, Cicero Mota: Fast and Easy Computation of Approximate Smallest Enclosing Balls. SIBGRAPI 2006: 163-170
- 2005
- [j6]Anke Meyer-Bäse, Karsten Jancke, Axel Wismüller, Simon Foo, Thomas Martinetz: Medical image compression using topology-preserving neural networks. Eng. Appl. of AI 18(4): 383-392 (2005)
- [j5]Amir Madany Mamlouk, Hannah Sharp, Kerstin M. L. Menne, Ulrich G. Hofmann, Thomas Martinetz: Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations. Neurocomputing 65-66: 275-282 (2005)
- [c13]Michael Dorr, Thomas Martinetz, Martin Böhme, Erhardt Barth: Visibility of temporal blur on a gaze-contingent display. APGV 2005: 33-36
- [c12]Thomas Martinetz, Kai Labusch, Daniel Schneegaß: SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification. ICANN (2) 2005: 301-306
- [e1]Uwe Brinkschulte, Jürgen Becker, Dietmar Fey, Christian Hochberger, Thomas Martinetz, Christian Müller-Schloer, Hartmut Schmeck, Theo Ungerer, Rolf P. Würtz (Eds.): 18th International Conference on Architecture of Computing Systems, Workshops, Innsbruck, Austria, March 2005. VDE Verlag 2005, ISBN 3-8007-2880-X
- 2004
- [j4]Jan T. Kim, Jan E. Gewehr, Thomas Martinetz: Binding Matrix: a Novel Approach for Binding Site Recognition. J. Bioinformatics and Computational Biology 2(2): 289-308 (2004)
- [c11]Thomas Martinetz: MinOver Revisited for Incremental Support-Vector-Classification. DAGM-Symposium 2004: 187-194
- [c10]Martin Böhme, Christopher Krause, Thomas Martinetz, Erhardt Barth: Saliency Extraction for Gaze-Contingent Displays. GI Jahrestagung (2) 2004: 646-650
- 2003
- [c9]Amir Madany Mamlouk, Jan T. Kim, Erhardt Barth, Michael Brauckmann, Thomas Martinetz: One-Class Classification with Subgaussians. DAGM-Symposium 2003: 346-353
- [c8]Anke Meyer-Bäse, Thomas D. Otto, Thomas Martinetz, Dorothee Auer, Axel Wismüller: Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis. ESANN 2003: 509-514
- 2001
- [c7]Daniel Polani, Thomas Martinetz, Jan T. Kim: An Information-Theoretic Approach for the Quantification of Relevance. ECAL 2001: 704-713
- [c6]Jan T. Kim, Thomas Martinetz, Daniel Polani: On the Effects of Transcription Factor Properties on the Information Content of Binding Sites. German Conference on Bioinformatics 2001: 192-194
- [c5]Martin Haker, André Meyer, Daniel Polani, Thomas Martinetz: A Method for Incorporation of New Evidence to Improve World State Estimation. RoboCup 2001: 362-367
- 2000
- [c4]Daniel Polani, Thomas Martinetz: Team Description for Lucky Lübeck - Evidence-Based World State Estimation. RoboCup 2000: 481-484
1990 – 1999
- 1999
- [c3]Claus O. Wilke, Christopher Ronnewinkel, Thomas Martinetz: Molecular Evolution in Time-Dependent Environments. ECAL 1999: 417-421
- [i1]Christopher Ronnewinkel, Claus O. Wilke, Thomas Martinetz: Genetic Algorithms in Time-Dependent Environments. CoRR physics/9911006 (1999)
- 1997
- [j3]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz: Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Trans. Neural Netw. Learning Syst. 8(2): 256-266 (1997)
- 1994
- [j2]Thomas Martinetz, Klaus Schulten: Topology representing networks. Neural Networks 7(3): 507-522 (1994)
- [c2]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz: Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. Fuzzy Days 1994: 159-166
- 1992
- [b3]Helge Ritter, Thomas Martinetz, Klaus Schulten: Neural computation and self-organizing maps - an introduction. Computation and neural systems series, Addison-Wesley 1992, ISBN 978-0-201-55443-4, pp. 1-306
- [b2]Thomas Martinetz: Selbstorganisierende neuronale Netzwerkmodelle zur Bewegungssteuerung. DISKI 14, Infix Verlag, St. Augustin, Germany 1992, ISBN 3-929037-14-9
- 1991
- [b1]Helge Ritter, Thomas Martinetz, Klaus Schulten: Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierter Netzwerke (2. Aufl.). Reihe Künstliche Intelligenz, Addison-Wesley 1991, ISBN 978-3-89319-131-4, pp. 1-325
- [c1]Stan Berkovitch, Philippe Dalger, Ted Hesselroth, Thomas Martinetz, Benoît Noël, Jörg A. Walter, Klaus Schulten: Vector Quantization Algorithm for Time Series Prediction and Visuo-Motor Control of Robots. Wissensbasierte Systeme 1991: 443-447
1980 – 1989
- 1989
- [j1]Helge Ritter, Thomas Martinetz, Klaus Schulten: Topology-conserving maps for learning visuo-motor-coordination. Neural Networks 2(3): 159-168 (1989)
Coauthor Index
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last updated on 2013-10-02 11:13 CEST by the dblp team