Téléchargez le livre : COLT '89

COLT '89

Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989

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Éditeur :

Morgan Kaufmann


Paru le : 2014-06-28



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Description
Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.
Pages
389 pages
Collection
n.c
Parution
2014-06-28
Marque
Morgan Kaufmann
EAN papier
9781558600867
EAN PDF SANS DRM
9780080948294
Informations sur l'ebook
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Prix
26,32 €

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