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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning

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Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers (Paperback, 2006 ed.) Loot Price: R1,540
Discovery Miles 15 400
Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual...

Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers (Paperback, 2006 ed.)

Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence D'Alche-Buc

Series: Lecture Notes in Artificial Intelligence, 3944

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Loot Price R1,540 Discovery Miles 15 400 | Repayment Terms: R144 pm x 12*

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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.

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Lecture Notes in Artificial Intelligence, 3944
Release date: May 2006
First published: 2006
Editors: Joaquin Quinonero-Candela • Ido Dagan • Bernardo Magnini • Florence D'Alche-Buc
Dimensions: 235 x 155 x 25mm (L x W x T)
Format: Paperback
Pages: 462
Edition: 2006 ed.
ISBN-13: 978-3-540-33427-9
Categories: Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN: 3-540-33427-0
Barcode: 9783540334279

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