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

Lecture Notes in Computer Science Band 3944

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

Buch (Taschenbuch, Englisch)
Buch (Taschenbuch, Englisch)
Fr. 99.90
Fr. 99.90
inkl. gesetzl. MwSt.
inkl. gesetzl. MwSt.
Versandfertig innert 1 - 2 Werktagen Versandkostenfrei
Versandfertig innert 1 - 2 Werktagen
Versandkostenfrei

Weitere Formate

Taschenbuch

Fr. 99.90

Accordion öffnen
  • Machine Learning Challenges

    Springer Berlin

    Versandfertig innert 1 - 2 Werktagen

    Fr. 99.90

    Springer Berlin

eBook (PDF)

Fr. 127.90

Accordion öffnen
  • Machine Learning Challenges

    PDF (Springer)

    Sofort per Download lieferbar

    Fr. 127.90

    PDF (Springer)

Beschreibung


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.

Produktdetails

Einband Taschenbuch
Erscheinungsdatum 11.05.2006
Verlag Springer Berlin
Seitenzahl 462
Maße (L/B/H) 23.5/15.5/2.9 cm
Gewicht 815 g
Auflage 2006
Sprache Englisch
ISBN 978-3-540-33427-9

Weitere Bände von Lecture Notes in Computer Science

Kundenbewertungen

Es wurden noch keine Bewertungen geschrieben.
  • artikelbild-0
  • 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?.