Artificial Neural Networks in Pattern Recognition: Second Iapr Workshop, Annpr 2006, Ulm, Germany, August 31-september 2, 2006, Proceedings by Friedhelm SchwenkerArtificial Neural Networks in Pattern Recognition: Second Iapr Workshop, Annpr 2006, Ulm, Germany, August 31-september 2, 2006, Proceedings by Friedhelm Schwenker

Artificial Neural Networks in Pattern Recognition: Second Iapr Workshop, Annpr 2006, Ulm, Germany…

byFriedhelm SchwenkerEditorSimone Marinai

Paperback | August 29, 2006

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This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics.

Title:Artificial Neural Networks in Pattern Recognition: Second Iapr Workshop, Annpr 2006, Ulm, Germany…Format:PaperbackDimensions:302 pages, 23.5 × 15.5 × 1.73 inPublished:August 29, 2006Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3540379517

ISBN - 13:9783540379515

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Table of Contents

Unsupervised Learning.- Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions.- Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition.- Adaptive Feedback Inhibition Improves Pattern Discrimination Learning.- Semi-supervised Learning.- Supervised Batch Neural Gas.- Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.- On the Effects of Constraints in Semi-supervised Hierarchical Clustering.- A Study of the Robustness of KNN Classifiers Trained Using Soft Labels.- Supervised Learning.- An Experimental Study on Training Radial Basis Functions by Gradient Descent.- A Local Tangent Space Alignment Based Transductive Classification Algorithm.- Incremental Manifold Learning Via Tangent Space Alignment.- A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware.- Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks.- Support Vector Learning.- Support Vector Regression Using Mahalanobis Kernels.- Incremental Training of Support Vector Machines Using Truncated Hypercones.- Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques.- Multiple Classifier Systems.- Multiple Classifier Systems for Embedded String Patterns.- Multiple Neural Networks for Facial Feature Localization in Orientation-Free Face Images.- Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory.- Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization.- Visual Object Recognition.- Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks.- Visual Classification of Images by Learning Geometric Appearances Through Boosting.- An Eye Detection System Based on Neural Autoassociators.- Orientation Histograms for Face Recognition.- Data Mining in Bioinformatics.- An Empirical Comparison of Feature Reduction Methods in the Context of Microarray Data Classification.- Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data.- Learning and Feature Selection Using the Set Covering Machine with Data-Dependent Rays on Gene Expression Profiles.