Machine Learning Challenges Evaluating Predictive Uncertainty Visual Object Classification and Recognizing Textual Entailment

This book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005. The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; the second challenge was to recognize objects from a number of visual object classes in realistic scenes; the third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.Quinonero-Candela, Joaquin is the author of 'Machine Learning Challenges Evaluating Predictive Uncertainty Visual Object Classification and Recognizing Textual Entailment', published 2006 under ISBN 9783540334279 and ISBN 3540334270.