Machine Learning Challenges

Joaquin Quinonero-Candela

ISBN 10: 3540334270 / ISBN 13: 9783540334279

Published by Springer, Springer Mai 2006, 2006

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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 .Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 484 pp. Englisch.

Seller Inventory # 9783540334279

Bibliographic Details

• Title: Machine Learning Challenges
• Publisher: Springer, Springer Mai 2006
• Publication Date: 2006
• Language: English
• ISBN 10: 3540334270
• ISBN 13: 9783540334279
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 30D millimeters 235 millimeters width by 155 millimeters height by 30 millimeters depth
• Weight: 815 grams

About this title

Synopsis

This book constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.