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MLCW 2005: Southampton, UK
- Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc
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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 3944, Springer 2006, ISBN 3-540-33427-0 - Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf:
Evaluating Predictive Uncertainty Challenge. 1-27 - Radford M. Neal:
Classification with Bayesian Neural Networks. 28-32 - Iain Murray, Edward Lloyd Snelson:
A Pragmatic Bayesian Approach to Predictive Uncertainty. 33-40 - Nitesh V. Chawla
Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees. 41-55 - Gavin C. Cawley, Nicola L. C. Talbot, Olivier Chapelle:
Estimating Predictive Variances with Kernel Ridge Regression. 56-77 - Shuichi Kurogi, Miho Sawa, Shinya Tanaka:
Competitive Associative Nets and Cross-Validation for Estimating Predictive Uncertainty on Regression Problems. 78-94 - Jukka Kohonen, Jukka Suomela
Lessons Learned in the Challenge: Making Predictions and Scoring Them. 95-116 - Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen
The 2005 PASCAL Visual Object Classes Challenge. 117-176 - Ido Dagan, Oren Glickman
The PASCAL Recognising Textual Entailment Challenge. 177-190 - Diana Pérez, Enrique Alfonseca:
Using Bleu-like Algorithms for the Automatic Recognition of Entailment. 191-204 - Lucy Vanderwende, William B. Dolan:
What Syntax Can Contribute in the Entailment Task. 205-216 - Milen Kouylekov, Bernardo Magnini
Combining Lexical Resources with Tree Edit Distance for Recognizing Textual Entailment. 217-230 - Jesús Herrera
Textual Entailment Recognition Based on Dependency Analysis and WordNet. 231-239 - Maria Teresa Pazienza, Marco Pennacchiotti, Fabio Massimo Zanzotto
Learning Textual Entailment on a Distance Feature Space. 240-260 - Rodrigo de Salvo Braz, Roxana Girju, Vasin Punyakanok, Dan Roth, Mark Sammons:
An Inference Model for Semantic Entailment in Natural Language. 261-286 - Oren Glickman
A Lexical Alignment Model for Probabilistic Textual Entailment. 287-298 - Dekai Wu
Textual Entailment Recognition Using Inversion Transduction Grammars. 299-308 - Samuel Bayer, John D. Burger, Lisa Ferro, John C. Henderson, Lynette Hirschman, Alexander S. Yeh:
Evaluating Semantic Evaluations: How RTE Measures Up. 309-331 - Alina Andreevskaia, Zhuoyan Li, Sabine Bergler:
Partial Predicate Argument Structure Matching for Entailment Determination. 332-343 - Rodolfo Delmonte
VENSES - A Linguistically-Based System for Semantic Evaluation. 344-371 - Eamonn Newman, Nicola Stokes, John Dunnion, Joe Carthy:
Textual Entailment Recognition Using a Linguistically-Motivated Decision Tree Classifier. 372-384 - Elena Akhmatova, Diego Mollá
Recognizing Textual Entailment Via Atomic Propositions. 385-403 - Johan Bos, Katja Markert:
Recognising Textual Entailment with Robust Logical Inference. 404-426 - Daniel Hodges, Christine Clark, Abraham Fowler, Dan I. Moldovan:
Applying COGEX to Recognize Textual Entailment. 427-448 - Valentin Jijkoun, Maarten de Rijke:
Recognizing Textual Entailment: Is Word Similarity Enough?. 449-460
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