PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Evaluating Predictive Uncertainty, Visual Objects Classification and Recognising textual entailment : selected proceedings of the First PASCAL Machine Learning Challenges Workshop.
Joaquin Quinonero Candela, Ido Dagan, Pierre Comon and Florence d'Alché-Buc, ed. (2006) Lectures notes in Artificial Intelligence , Volume 3944 . Springer Verlag . ISBN 3 540 33427 0


The first PASCAL Machine Learning Challenges Workshop (MLCW 2005) was held in Southampton, UK, during April 11-13, 2005. This conference ( was organized by the Challenges programme of the European Network of Excellence PASCAL (Pattern Analysis, Statistical modelling and ComputationAl Learning) in the framework of the IST Programme of the European Community. First annually and now quarterly, the PASCAL Challenges Programme plays the role of selecting and sponsoring challenging tasks, either practical or theoretical. The aim is to raise difficult machine learning questions and to motivate innovative research and development of new approaches. Financial support covers all the work concerning the cleaning and labelling of the data as well as the preparation of evaluation tools for ranking the results. For the first round of the Programme, four challenges were selected according to their impact on the machine learning community, supported from summer 2004 to early spring 2005 by PASCAL and finally invited to participate in MLCW'05 : - the first challenge, called Evaluating Predictive Uncertainty dealt with the fundamental question of assigning a degree of confidence to the outputs of a classifier or a regressor. - the goal of the second challenge, called Visual Object Classes, was to recognise objects from a number of visual object classes in realistic scenes. - the third challenge task, called Recognising Textual Entailment, consisted in recognising, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. - the fourth challenge was concerned with the assessment of Machine Learning methodologies to extract implicit relations from documents. Each of these challenges raised notable attention in the research community, attracting numerous participants. The idea behind having a unique workshop was to enable participants in different challenges to exchange and benefit from the research experience in other challenges. For the workshop, the session chairs operated a first selection among submissions leading to 34 oral contributions. This book is concerned with selected proceedings of the three first challenges, providing a large panel of machine learning issues and solutions. A second round of selection was applied to extract the 25 contributed chapters that compose this book, resulting in a selection rate of a half.

EPrint Type:Book
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:2748
Deposited By:Florence d'Alché-Buc
Deposited On:22 November 2006