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 / / edited by Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006
• Edizione: 1st ed. 2006.
• Descrizione fisica: 1 online resource (XIII, 462 p.)
• Disciplina: 006.3/1
• Soggetto topico: Artificial intelligence; Algorithms; Machine theory; Natural language processing (Computer science); Computer vision; Pattern recognition systems; Artificial Intelligence; Formal Languages and Automata Theory; Natural Language Processing (NLP); Computer Vision; Automated Pattern Recognition
• Altri autori: Quinonero-CandelaJoaquin
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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?.
• Sommario/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.
• Titolo autorizzato: Machine learning challenges
• ISBN: 3-540-33428-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910484524603321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 3944