05104nam 22008175 450 991048452460332120251226202945.03-540-33428-910.1007/11736790(CKB)1000000000232927(SSID)ssj0000318735(PQKBManifestationID)11240010(PQKBTitleCode)TC0000318735(PQKBWorkID)10311626(PQKB)10452653(DE-He213)978-3-540-33428-6(MiAaPQ)EBC3067600(PPN)123133548(BIP)34164024(BIP)13336740(EXLCZ)99100000000023292720100301d2006 u| 0engurnn|008mamaatxtccrMachine 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-Buc1st ed. 2006.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2006.1 online resource (XIII, 462 p.) Lecture Notes in Artificial Intelligence,2945-9141 ;3944Bibliographic Level Mode of Issuance: Monograph3-540-33427-0 Includes bibliographical references and index.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?.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.Lecture Notes in Artificial Intelligence,2945-9141 ;3944Artificial intelligenceAlgorithmsMachine theoryNatural language processing (Computer science)Computer visionPattern recognition systemsArtificial IntelligenceAlgorithmsFormal Languages and Automata TheoryNatural Language Processing (NLP)Computer VisionAutomated Pattern RecognitionArtificial intelligence.Algorithms.Machine theory.Natural language processing (Computer science).Computer vision.Pattern recognition systems.Artificial Intelligence.Algorithms.Formal Languages and Automata Theory.Natural Language Processing (NLP).Computer Vision.Automated Pattern Recognition.006.3/1Quinonero-Candela Joaquin518515MiAaPQMiAaPQMiAaPQBOOK9910484524603321Machine learning challenges4184373UNINA