Algebraic Geometry and Statistical Learning Theory

Sumio Watanabe

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Language: English

Published by Cambridge University Press, 2009

0521864674 / 9780521864671

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Druck auf Anfrage Neuware - Printed after ordering - Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.…

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Title
Algebraic Geometry and Statistical Learning Theory
Author
Sumio Watanabe
Publisher
Cambridge University Press
Publication year
2009
Condition
Neu
Binding
Buch
Language
English
ISBN 10
0521864674
ISBN 13
9780521864671
Item weight
637 grams
Dimensions
235x157x22 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

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Item7 to 10 business days5 to 7 business days
First itemUS$ 39.87US$ 51.26
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