PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Subspace, Latent Structure and Feature Selection techniques
Craig Saunders, Steve Gunn, Marko Grobelnik and John Shawe-Taylor, ed. (2006) Lecture Notes in Computer Science , Volume 3940 . Springer . ISBN 978-3-540-34137-6

Abstract

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005, held in Bohinj, Slovenia in February 2005. The 9 revised full papers presented together with 5 invited papers were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, statistical analysis methods, Bayesian approaches to feature selection, latent structure analysis/probabilistic LSA, and optimisation methods.

EPrint Type:Book
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4052
Deposited By:Craig Saunders
Deposited On:25 February 2008