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
Published on 11. May 2006
Book
Paperback/Softback
XIII, 462 pages
978-3-540-33427-9 (ISBN)
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.
More details
Series
Edition
2006 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XIII, 462 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 30 mm
Weight
815 gr
ISBN-13
978-3-540-33427-9 (9783540334279)
DOI
10.1007/11736790
Schweitzer Classification
Content
Evaluating Predictive Uncertainty Challenge.- Classification with Bayesian Neural Networks.- A Pragmatic Bayesian Approach to Predictive Uncertainty.- Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees.- Estimating Predictive Variances with Kernel Ridge Regression.- Competitive Associative Nets and Cross-Validation for Estimating Predictive Uncertainty on Regression Problems.- Lessons Learned in the Challenge: Making Predictions and Scoring Them.- The 2005 PASCAL Visual Object Classes Challenge.- The PASCAL Recognising Textual Entailment Challenge.- Using Bleu-like Algorithms for the Automatic Recognition of Entailment.- What Syntax Can Contribute in the Entailment Task.- Combining Lexical Resources with Tree Edit Distance for Recognizing Textual Entailment.- Textual Entailment Recognition Based on Dependency Analysis and WordNet.- Learning Textual Entailment on a Distance Feature Space.- An Inference Model for Semantic Entailment in Natural Language.- A Lexical Alignment Model for Probabilistic Textual Entailment.- Textual Entailment Recognition Using Inversion Transduction Grammars.- Evaluating Semantic Evaluations: How RTE Measures Up.- Partial Predicate Argument Structure Matching for Entailment Determination.- VENSES - A Linguistically-Based System for Semantic Evaluation.- Textual Entailment Recognition Using a Linguistically-Motivated Decision Tree Classifier.- Recognizing Textual Entailment Via Atomic Propositions.- Recognising Textual Entailment with Robust Logical Inference.- Applying COGEX to Recognize Textual Entailment.- Recognizing Textual Entailment: Is Word Similarity Enough?.