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Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth 2009-11-02 · Hello Select your address Best Sellers Today's Deals New Releases Gift Ideas Books Electronics Customer Service Home Computers Gift Cards Sell Algebraic Geometry and Statistical Learning Theory: 25: Watanabe, Sumio: Amazon.sg: Books Skip to main content.sg. Hello Select your address Algebraic Geometry And Statistical Learning Theory related files: 9dba62bf5e0f9353cf40cd1edaa34f00 Powered by TCPDF (www.tcpdf.org) 1 / 1 Buy Algebraic Geometry and Statistical Learning Theory by Watanabe, Sumio online on Amazon.ae at best prices. Fast and free shipping free returns cash on delivery available on eligible purchase. This article introduces the book, “algebraic geometry and statistical learning theory. ” A parametric model in statistics or a learning machine in information science is called singular if it is not identifiable or if its Fisher information matrix is not positive definite. Although a lot of statistical models and learning machines are singular, their statistical properties have been left Algebraic geometry and number theory.
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Cambridge University Press, Algebraic Geometry and Statistical Learning Theory The Cambridge Monographs on Applied and Computational Mathematics reflect the crucial role of mathematical and computational techniques in contemporary science. Algebraic geometry and singularity theory provide the mathematical foundation on which a new In this talk, we give a basic introduction to Sumio Watanabe's Singular Learning Theory, as outlined in his book "Algebraic Geometry and Statistical Learning Theory". Watanabe's key insight to studying singular models was to use a deep result in algebraic geometry known as Hironaka's Resolution of Singularities.
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Q325.7.W38 2009 006.3 1 – dc22 2009011366 ISBN 978-0-521-86467-1 hardback Algebraic Geometry and Statistical Learning Theory 1st Edition by Sumio Watanabe and Publisher Cambridge University Press. Save up to 80% by choosing the eTextbook option for ISBN: 9781107713963, 110771396X. The print version of this textbook is ISBN: 9780521864671, 0521864674. Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory.Many widely used statistical models are singular: mixture models, neural networks, HMMs, and Bayesian networks are major examples.