9783540334279-3540334270-Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine ... (Lecture Notes in Computer Science, 3944)

Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine ... (Lecture Notes in Computer Science, 3944)

ISBN-13: 9783540334279
ISBN-10: 3540334270
Edition: 2006
Author: Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence dAlché-Buc
Publication date: 2006
Publisher: Springer
Format: Paperback 475 pages
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Book details

ISBN-13: 9783540334279
ISBN-10: 3540334270
Edition: 2006
Author: Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence dAlché-Buc
Publication date: 2006
Publisher: Springer
Format: Paperback 475 pages

Summary

Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine ... (Lecture Notes in Computer Science, 3944) (ISBN-13: 9783540334279 and ISBN-10: 3540334270), written by authors Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence dAlché-Buc, was published by Springer in 2006. With an overall rating of 3.6 stars, it's a notable title among other books. You can easily purchase or rent Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine ... (Lecture Notes in Computer Science, 3944) (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 refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.

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