ENGLISH

Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers

By Joaquin Quiñonero-Candela (auth.) , Joaquin Quiñonero-Candela , Ido Dagan , Bernardo Magnini , Florence d’Alché-Buc (eds.)

Book information

Publisher
Springer-Verlag Berlin Heidelberg
Year
2006
ISBN
978-3-540-33427-9, 978-3-540-33428-6
DOI
10.1007/11736790
Language
english
Format
PDF
Filesize
4 MB (4555148 bytes)
Series
Lecture Notes in Computer Science 3944
Edition
1
Pages
462\473
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Artificial Intelligence (incl. Robotics); Algorith

Description

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.

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