This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. "This book compiles some very promising techniques, coming from an extremely smart collection of researchers, delivering their best ideas in a competitive environment." Trevor Hastie, Stanford University "Feature selection is a key technology for making sense of the high dimensional data. Isabelle Guyon et al. have done a splendid job in designing a challenging competition, and collecting the lessons learned." Bernhard Schoelkopf, Max Planck Institute "There has been until now insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons. This volume is noteworthy for the breadth of methods covered, the clarity of presentations, the unity in notation and the helpful statistical appendices." David G. Stork, Ricoh Innovations "Feature extraction finds application in biotechnology, industrial inspection, the Internet, radar, sonar, and speech recognition. This book will make a difference to the literature on machine learning." Simon Haykin, Mc Master University "This book sets a high standard as the public record of an interesting and effective competition." Peter Norvig, Google Inc.

Inhalt

An Introduction to Feature Extraction.- An Introduction to Feature Extraction.- Feature Extraction Fundamentals.- Learning Machines.- Assessment Methods.- Filter Methods.- Search Strategies.- Embedded Methods.- Information-Theoretic Methods.- Ensemble Learning.- Fuzzy Neural Networks.- Feature Selection Challenge.- Design and Analysis of the NIPS2003 Challenge.- High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees.- Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems.- Combining SVMs with Various Feature Selection Strategies.- Feature Selection with Transductive Support Vector Machines.- Variable Selection using Correlation and Single Variable Classifier Methods: Applications.- Tree-Based Ensembles with Dynamic Soft Feature Selection.- Sparse, Flexible and Efficient Modeling using L 1 Regularization.- Margin Based Feature Selection and Infogain with Standard Classifiers.- Bayesian Support Vector Machines for Feature Ranking and Selection.- Nonlinear Feature Selection with the Potential Support Vector Machine.- Combining a Filter Method with SVMs.- Feature Selection via Sensitivity Analysis with Direct Kernel PLS.- Information Gain, Correlation and Support Vector Machines.- Mining for Complex Models Comprising Feature Selection and Classification.- Combining Information-Based Supervised and Unsupervised Feature Selection.- An Enhanced Selective Naïve Bayes Method with Optimal Discretization.- An Input Variable Importance Definition based on Empirical Data Probability Distribution.- New Perspectives in Feature Extraction.- Spectral Dimensionality Reduction.- Constructing Orthogonal Latent Features for Arbitrary Loss.- Large Margin Principles for Feature Selection.- Feature Extraction for Classification of Proteomic Mass Spectra: A Comparative Study.- Sequence Motifs: Highly Predictive Features of Protein Function.