Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop

Quinonero-candela, Joaquin (Editor)/ Dagan, Ido (Editor)/ Magnini, Bernardo (Editor)/ D'alche-buc, Florence (Editor)

ISBN 10: 3540334270 / ISBN 13: 9783540334279

Editore: Springer-Verlag New York Inc, 2006

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1st edition. 462 pages. 9.25x6.25x0.75 inches. In Stock.

Codice articolo x-3540334270

Dati bibliografici

• Titolo: Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop
• Editore: Springer-Verlag New York Inc
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 3540334270
• ISBN 13: 9783540334279
• Rilegatura: Paperback
• Condizione: Brand New
• Peso: 1,77 chilogrammi

Descrizione articolo

Riassunto

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.

Contenuti

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?.