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 /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Quiñonero-Candela, Joaquin (Επιμελητής έκδοσης), Dagan, Ido (Επιμελητής έκδοσης), Magnini, Bernardo (Επιμελητής έκδοσης), d’Alché-Buc, Florence (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Σειρά: Lecture Notes in Computer Science, 3944
• Θέματα: Computer science.; Algorithms.; Mathematical logic.; Artificial intelligence.; Text processing (Computer science).; Image processing.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Algorithm Analysis and Problem Complexity.; Mathematical Logic and Formal Languages.; Document Preparation and Text Processing.; Image Processing and Computer Vision.; Pattern Recognition.

Πίνακας περιεχομένων:

• 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?.