9783540724810-3540724818-Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer Science, 4472)

Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer Science, 4472)

ISBN-13: 9783540724810
ISBN-10: 3540724818
Edition: 2007
Author: Fabio Roli, Josef Kittler, Michal Haindl
Publication date: 2007
Publisher: Springer
Format: Paperback 535 pages
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Book details

ISBN-13: 9783540724810
ISBN-10: 3540724818
Edition: 2007
Author: Fabio Roli, Josef Kittler, Michal Haindl
Publication date: 2007
Publisher: Springer
Format: Paperback 535 pages

Summary

Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer Science, 4472) (ISBN-13: 9783540724810 and ISBN-10: 3540724818), written by authors Fabio Roli, Josef Kittler, Michal Haindl, was published by Springer in 2007. With an overall rating of 4.1 stars, it's a notable title among other AI & Machine Learning (Graphics & Design, Network Security, Security & Encryption, Graphics & Multimedia, Programming, Software Design, Testing & Engineering, Computer Science) books. You can easily purchase or rent Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer Science, 4472) (Paperback) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.

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