ANNPR 2006: Ulm, Germany

Friedhelm Schwenker, Simone Marinai (Eds.): Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings. Lecture Notes in Computer Science 4087 Springer 2006, ISBN 3-540-37951-7

Unsupervised Learning

Edmondo Trentin:
Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions. 1-10
Zouhour Neji Ben Salem, Laurent Bougrain, Frédéric Alexandre:
Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition. 11-20
Frank Michler, Thomas Wachtler, Reinhard Eckhorn:
Adaptive Feedback Inhibition Improves Pattern Discrimination Learning. 21-32

Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann:
Supervised Batch Neural Gas. 33-45
Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer:
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. 46-56
Hans A. Kestler, Johann M. Kraus, Günther Palm, Friedhelm Schwenker:
On the Effects of Constraints in Semi-supervised Hierarchical Clustering. 57-66
Neamat El Gayar, Friedhelm Schwenker, Günther Palm:
A Study of the Robustness of KNN Classifiers Trained Using Soft Labels. 67-80

Joaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo:
An Experimental Study on Training Radial Basis Functions by Gradient Descent. 81-92
Jianwei Yin, Xiaoming Liu, Zhilin Feng, Jinxiang Dong:
A Local Tangent Space Alignment Based Transductive Classification Algorithm. 93-106
Xiaoming Liu, Jianwei Yin, Zhilin Feng, Jinxiang Dong:
Incremental Manifold Learning Via Tangent Space Alignment. 107-121
Johannes Fieres, Karlheinz Meier, Johannes Schemmel:
A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware. 122-132
Hilario López García, Iván Machón González:
Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks. 133-143

Yuya Kamada, Shigeo Abe:
Support Vector Regression Using Mahalanobis Kernels. 144-152
Shinya Katagiri, Shigeo Abe:
Incremental Training of Support Vector Machines Using Truncated Hypercones. 153-164
Yusuke Torii, Shigeo Abe:
Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques. 165-176

Barbara Spillmann, Michel Neuhaus, Horst Bunke:
Multiple Classifier Systems for Embedded String Patterns. 177-187
Lionel Prevost, Rachid Belaroussi, Maurice Milgram:
Multiple Neural Networks for Facial Feature Localization in Orientation-Free Face Images. 188-197
Rebecca Fay, Friedhelm Schwenker, Christian Thiel, Günther Palm:
Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory. 198-209
Joaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo:
Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization. 210-220

Alexander Gepperth:
Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks. 221-232
Martin Antenreiter, Christian Savu-Krohn, Peter Auer:
Visual Classification of Images by Learning Geometric Appearances Through Boosting. 233-243
Monica Bianchini, Lorenzo Sarti:
An Eye Detection System Based on Neural Autoassociators. 244-252
Friedhelm Schwenker, Andreas Sachs, Günther Palm, Hans A. Kestler:
Orientation Histograms for Face Recognition. 253-259

Hans A. Kestler, Christoph Müssel:
An Empirical Comparison of Feature Reduction Methods in the Context of Microarray Data Classification. 260-273
Marc Strickert, Nese Sreenivasulu, Silke Peterek, Winfriede Weschke, Hans-Peter Mock, Udo Seiffert:
Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data. 274-285
Hans A. Kestler, Wolfgang Lindner, André Müller:
Learning and Feature Selection Using the Set Covering Machine with Data-Dependent Rays on Gene Expression Profiles. 286-297

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