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Record Nr. |
UNINA9910484524603321 |
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Titolo |
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 |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
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ISBN |
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Edizione |
[1st ed. 2006.] |
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Descrizione fisica |
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1 online resource (XIII, 462 p.) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 3944 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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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 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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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?. |
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Sommario/riassunto |
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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. |
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