Machine Learning Challenges
Publication year: 2006
ISBN: 978-3-540-33428-6
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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.
Subject: Computer Science, Bayesian inference, Syntax, algorithm, algorithmic learning, algorithms, classification, cognition, computational learning, forecasting, heuristics, image recognition, inductive logic programming, kernel-based learning, machine learning, natural language processing
