Artificial Neural Networks in Pattern Recognition: Third IAPR TC3 Workshop, ANNPR 2008 Paris, France, July 2-4, 2008, Proceedings / Edition 1

by Lionel Prevost, Simone Marinai, Friedhelm Schwenker

Overview

This book constitutes the refereed proceedings of the Third TC3 IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008, held in Paris, France, in July 2008.

The 18 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 57 submissions. The papers combine many ideas from machine learning, advanced statistics, signal and image processing for solving complex real-world pattern recognition problems. The papers are organized in topical sections on unsupervised learning, supervised learning, multiple classifiers, applications, and feature selection.

Product Details

ISBN-13:	9783540699385
Publisher:	Springer Berlin Heidelberg
Publication date:	08/06/2008
Series:	Lecture Notes in Computer Science , #5064
Edition description:	2008
Pages:	322
Product dimensions:	6.10(w) x 9.30(h) x 0.80(d)

Table of Contents

Unsupervised Learning.- Patch Relational Neural Gas – Clustering of Huge Dissimilarity Datasets.- The Block Generative Topographic Mapping.- Kernel k-Means Clustering Applied to Vector Space Embeddings of Graphs.- Probabilistic Models Based on the—-Sigmoid Distribution.- How Robust Is a Probabilistic Neural VLSI System Against Environmental Noise.- Supervised Learning.- Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis.- Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network.- Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.- Neural Approximation of Monte Carlo Policy Evaluation Deployed in Connect Four.- Cyclostationary Neural Networks for Air Pollutant Concentration Prediction.- Fuzzy Evolutionary Probabilistic Neural Networks.- Experiments with Supervised Fuzzy LVQ.- A Neural Network Approach to Similarity Learning.- Partial Discriminative Training of Neural Networks for Classification of Overlapping Classes.- Multiple Classifiers.- Boosting Threshold Classifiers for High– Dimensional Data in Functional Genomics.- Decision Fusion on Boosting Ensembles.- The Mixture of Neural Networks as Ensemble Combiner.- Combining Methods for Dynamic Multiple Classifier Systems.- Researching on Multi-net Systems Based on Stacked Generalization.- Applications.- Real-Time Emotion Recognition from Speech Using Echo State Networks.- Sentence Understanding and Learning of New Words with Large-Scale Neural Networks.- Multi-class Vehicle Type Recognition System.- A Bio-inspired Neural Model for Colour Image Segmentation.- Mining Software Aging Patterns by Artificial Neural Networks.- Bayesian Classifiers for Predicting the Outcome of Breast Cancer Preoperative Chemotherapy.- Feature Selection.- Feature Ranking Ensembles for Facial Action Unit Classification.- Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration.- Improving Features Subset Selection Using Genetic Algorithms for Iris Recognition.- Artificial Neural Network Based Automatic Face Model Generation System from Only One Fingerprint.