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

Quinonero Candela, J; Dagan , I; Magnini, B; Lauria, F

doi:10.1007/11736790

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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

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Quinonero Candela, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Quinonero Candela, J., Dagan, I., Magnini, B., & Lauria, F. (2006). 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. Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D327-9

Abstract

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.