Machine Learning Challenges [[electronic resource] ] : 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

• Record Nr.: UNISA996466135103316
• Pubbl/distr/stampa: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006
• ISBN: 3-540-33428-9
• Edizione: [1st ed. 2006.]
• Descrizione fisica: 1 online resource (XIII, 462 p.)
• Collana: Lecture Notes in Artificial Intelligence ; ; 3944
• Disciplina: 006.3/1
• Soggetti: Artificial intelligence; Algorithms; Mathematical logic; Natural language processing (Computer science); Optical data processing; Pattern recognition; Artificial Intelligence; Algorithm Analysis and Problem Complexity; Mathematical Logic and Formal Languages; Natural Language Processing (NLP); Image Processing and Computer Vision; Pattern Recognition
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• 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?.