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

Joaquin Quinonero-Candela

ISBN 10: 3540334270 / ISBN 13: 9783540334279

Published by Springer Berlin Heidelberg, 2006

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783540334279

Bibliographic Details

• Title: 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
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2006
• Language: English
• ISBN 10: 3540334270
• ISBN 13: 9783540334279
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 30D millimeters 235 millimeters width by 155 millimeters height by 30 millimeters depth
• Weight: 815 grams

About this title

Synopsis

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.