Ebook: Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers
- Genre: Education // International Conferences and Symposiums
- Tags: Algorithm Analysis and Problem Complexity, Probability and Statistics in Computer Science, Computation by Abstract Devices, Artificial Intelligence (incl. Robotics), Image Processing and Computer Vision, Pattern Recognition
- Series: Lecture Notes in Computer Science 3940 : Theoretical Computer Science and General Issues
- Year: 2006
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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.
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.