9783540341376-3540341374-Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, ... (Lecture Notes in Computer Science, 3940)

Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, ... (Lecture Notes in Computer Science, 3940)

ISBN-13: 9783540341376
ISBN-10: 3540341374
Edition: 2006
Author: John Shawe-Taylor, Craig Saunders, Steve Gunn, Marko Grobelnik
Publication date: 2006
Publisher: Springer
Format: Paperback 219 pages
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Book details

ISBN-13: 9783540341376
ISBN-10: 3540341374
Edition: 2006
Author: John Shawe-Taylor, Craig Saunders, Steve Gunn, Marko Grobelnik
Publication date: 2006
Publisher: Springer
Format: Paperback 219 pages

Summary

Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, ... (Lecture Notes in Computer Science, 3940) (ISBN-13: 9783540341376 and ISBN-10: 3540341374), written by authors John Shawe-Taylor, Craig Saunders, Steve Gunn, Marko Grobelnik, was published by Springer in 2006. With an overall rating of 4.4 stars, it's a notable title among other books. You can easily purchase or rent Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, ... (Lecture Notes in Computer Science, 3940) (Paperback) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

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. The 9 revised full papers presented together with 5 invited papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, among others.

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