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Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
Title:
Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
Author:
Quiñonero-Candela, Joaquin. editor.
ISBN:
9783540334286
Physical Description:
XIII, 462 p. Also available online. online resource.
Series:
Lecture Notes in Computer Science, 3944
Contents:
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?.
Subject Term:
Added Corporate Author:
Electronic Access:
http://dx.doi.org/10.1007/11736790