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

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

Buch | Softcover

XIII, 462 Seiten

2006 | 2006
Springer Berlin (Verlag)
978-3-540-33427-9 (ISBN)

Artikel merken

lt;p>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.

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?.

Erscheint lt. Verlag	11.5.2006
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XIII, 462 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 235 mm
Gewicht	813 g
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	algorithm • Algorithm analysis and problem complexity • Algorithmic Learning • algorithms • Bayesian inference • classification • Cognition • Computational Learning • Forecasting • Heuristics • Image Recognition • Inductive Logic Programming • kernel-based learning • Learning Algorithms • machine learning • Natural Language Processing • Object recognition • Pattern Analysis • Segmentation • semantic inference • semantic language processing • Statistical Learning • Statistical Modelling • Syntax
ISBN-10	3-540-33427-0 / 3540334270
ISBN-13	978-3-540-33427-9 / 9783540334279
Zustand	Neuware