2009 |
41 | EE | Kai Labusch,
Erhardt Barth,
Thomas Martinetz:
Sparse Coding Neural Gas: Learning of overcomplete data representations.
Neurocomputing 72(7-9): 1547-1555 (2009) |
2008 |
40 | EE | Kai Labusch,
Fabian Timm,
Thomas Martinetz:
Simple Incremental One-Class Support Vector Classification.
DAGM-Symposium 2008: 21-30 |
39 | EE | Kai Labusch,
Erhardt Barth,
Thomas Martinetz:
Learning Data Representations with Sparse Coding Neural Gas.
ESANN 2008: 233-238 |
38 | EE | Martin Böhme,
Michael Dorr,
Mathis Graw,
Thomas Martinetz,
Erhardt Barth:
A software framework for simulating eye trackers.
ETRA 2008: 251-258 |
37 | EE | 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 |
36 | EE | Kai Labusch,
Erhardt Barth,
Thomas Martinetz:
Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources.
ICANN (1) 2008: 788-797 |
35 | EE | Fabian Timm,
Sascha Klement,
Thomas Martinetz:
Fast model selection for MaxMinOver-based training of support vector machines.
ICPR 2008: 1-4 |
34 | EE | Daniel Schneegaß,
Steffen Udluft,
Thomas Martinetz:
Uncertainty propagation for quality assurance in Reinforcement Learning.
IJCNN 2008: 2588-2595 |
2007 |
33 | EE | Daniel Schneegaß,
Steffen Udluft,
Thomas Martinetz:
Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments.
ESANN 2007: 301-306 |
32 | EE | Daniel Schneegaß,
Anton Maximilian Schäfer,
Thomas Martinetz:
The Intrinsic Recurrent Support Vector Machine.
ESANN 2007: 325-330 |
31 | EE | Daniel Schneegaß,
Steffen Udluft,
Thomas Martinetz:
Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification.
ESANN 2007: 337-342 |
30 | EE | 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 |
29 | | Daniel Schneegaß,
Steffen Udluft,
Thomas Martinetz:
Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach.
Artificial Intelligence and Applications 2006: 428-433 |
28 | EE | Daniel Schneegaß,
Thomas Martinetz,
Michael Clausohm:
OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method.
ESANN 2006: 575-580 |
27 | EE | Martin Böhme,
Michael Dorr,
Thomas Martinetz,
Erhardt Barth:
Gaze-contingent temporal filtering of video.
ETRA 2006: 109-115 |
26 | EE | Daniel Schneegaß,
Kai Labusch,
Thomas Martinetz:
MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation.
ICANN (1) 2006: 150-158 |
25 | EE | Erhardt Barth,
Michael Dorr,
Martin Böhme,
Karl R. Gegenfurtner,
Thomas Martinetz:
Guiding Eye Movements for Better Communication and Augmented Vision.
PIT 2006: 1-8 |
24 | EE | Michael Dorr,
Martin Böhme,
Thomas Martinetz,
Erhardt Barth:
Gaze-Contingent Spatio-temporal Filtering in a Head-Mounted Display.
PIT 2006: 205-207 |
23 | EE | André Meyer,
Martin Böhme,
Thomas Martinetz,
Erhardt Barth:
A Single-Camera Remote Eye Tracker.
PIT 2006: 208-211 |
22 | EE | Thomas Martinetz,
Amir Madany Mamlouk,
Cicero Mota:
Fast and Easy Computation of Approximate Smallest Enclosing Balls.
SIBGRAPI 2006: 163-170 |
21 | EE | Martin Böhme,
Michael Dorr,
Christopher Krause,
Thomas Martinetz,
Erhardt Barth:
Eye movement predictions on natural videos.
Neurocomputing 69(16-18): 1996-2004 (2006) |
2005 |
20 | | Uwe Brinkschulte,
Jürgen Becker,
Dietmar Fey,
Christian Hochberger,
Thomas Martinetz,
Christian Müller-Schloer,
Hartmut Schmeck,
Theo Ungerer,
Rolf P. Würtz:
18th International Conference on Architecture of Computing Systems, Workshops, Innsbruck, Austria, March 2005
VDE Verlag 2005 |
19 | EE | Michael Dorr,
Thomas Martinetz,
Martin Böhme,
Erhardt Barth:
Visibility of temporal blur on a gaze-contingent display.
APGV 2005: 33-36 |
18 | EE | Thomas Martinetz,
Kai Labusch,
Daniel Schneegaß:
SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification.
ICANN (2) 2005: 301-306 |
17 | EE | 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) |
2004 |
16 | EE | Thomas Martinetz:
MinOver Revisited for Incremental Support-Vector-Classification.
DAGM-Symposium 2004: 187-194 |
15 | | Martin Böhme,
Christopher Krause,
Thomas Martinetz,
Erhardt Barth:
Saliency Extraction for Gaze-Contingent Displays.
GI Jahrestagung (2) 2004: 646-650 |
14 | EE | 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) |
2003 |
13 | EE | Amir Madany Mamlouk,
Jan T. Kim,
Erhardt Barth,
Michael Brauckmann,
Thomas Martinetz:
One-Class Classification with Subgaussians.
DAGM-Symposium 2003: 346-353 |
12 | EE | 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 |
11 | EE | Daniel Polani,
Thomas Martinetz,
Jan T. Kim:
An Information-Theoretic Approach for the Quantification of Relevance.
ECAL 2001: 704-713 |
10 | | 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 |
9 | EE | 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 |
8 | EE | Daniel Polani,
Thomas Martinetz:
Team Description for Lucky Lübeck - Evidence-Based World State Estimation.
RoboCup 2000: 481-484 |
1999 |
7 | | Claus O. Wilke,
Christopher Ronnewinkel,
Thomas Martinetz:
Molecular Evolution in Time-Dependent Environments.
ECAL 1999: 417-421 |
6 | EE | Christopher Ronnewinkel,
Claus O. Wilke,
Thomas Martinetz:
Genetic Algorithms in Time-Dependent Environments
CoRR physics/9911006: (1999) |
1994 |
5 | | 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 |
4 | EE | Thomas Martinetz,
Klaus Schulten:
Topology representing networks.
Neural Networks 7(3): 507-522 (1994) |
1992 |
3 | | Thomas Martinetz:
Selbstorganisierende neuronale Netzwerkmodelle zur Bewegungssteuerung.
Infix Verlag, St. Augustin, Germany 1992 |
1991 |
2 | | 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 |
1989 |
1 | EE | Helge Ritter,
Thomas Martinetz,
Klaus Schulten:
Topology-conserving maps for learning visuo-motor-coordination.
Neural Networks 2(3): 159-168 (1989) |