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Algebraic geometry and singularity theory provide the mathematical foundation on which a new statistical learning theory is constructed. For example, resolution of singularities is a powerful method which makes the log likelihood function be a common standard form.

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Computational learning theory – Statistical methods. 2. Geometry, Algebraic. I. Title. 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.

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Algebraic Geometry and Statistical Learning Theory 作者 : Sumio Watanabe 出版社: Cambridge University Press 出版年: 2009-8-13 页数: 300 定价: GBP 57.00 装帧: Hardcover ISBN: 9780521864671

Q325.7.W38 2009 006.3 1 – dc22 2009011366 ISBN 978-0-521-86467-1 hardback ‎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 n… Algebraic Geometry and Statistical Learning Theory 作者 : Sumio Watanabe 出版社: Cambridge University Press 出版年: 2009-8-13 页数: 300 定价: GBP 57.00 装帧: Hardcover ISBN: 9780521864671 Application of algebraic geometry to statistical learning theory Algebraic geometry has also recently found applications to statistical learning theory, including a generalization of the Akaike information criterion to singular statistical models.

2020-10-27 · Algebraic geometry and number theory The group conducts research in a diverse selection of topics in algebraic geometry and number theory. Areas of interest and activity include, but are not limited to: Clifford algebras, Arakelov geometry, additive number theory, combinatorial number theory, automorphic forms, L-functions, singularities, rational points on varieties, and algebraic surfaces.

inbunden, 2009. Skickas inom 5-16 vardagar. Köp boken Algebraic Geometry and Statistical Learning Theory av Sumio Watanabe (ISBN Pris: 659 kr. Inbunden, 2009. Skickas inom 7-10 vardagar. Köp Algebraic Geometry and Statistical Learning Theory av Sumio Watanabe på Bokus.com. Pris: 649 kr.

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 n… Algebraic Geometry and Statistical Learning Theory 作者 : Sumio Watanabe 出版社: Cambridge University Press 出版年: 2009-8-13 页数: 300 定价: GBP 57.00 装帧: Hardcover ISBN: 9780521864671 Application of algebraic geometry to statistical learning theory Algebraic geometry has also recently found applications to statistical learning theory, including a generalization of the Akaike information criterion to singular statistical models. Statistical learning theory is now a well-established subject, and has found practical use in artificial intelligence as well as a framework for studying computational learning theory. There are many fine books on the subject, but this one studies it from the standpoint of algebraic geometry, a field which decades ago was deemed too esoteric for use in the real world but is now embedded in myriads of applications. 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. More specifically, the author uses the resolution of singularities theorem from real algebraic geometry to study statistical learning theory when the parameter space is highly singular.
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I. Title. Q325.7.W38 2009 006.3 1 – dc22 2009011366 ISBN 978-0-521-86467-1 hardback ‎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 n… Algebraic Geometry and Statistical Learning Theory 作者 : Sumio Watanabe 出版社: Cambridge University Press 出版年: 2009-8-13 页数: 300 定价: GBP 57.00 装帧: Hardcover ISBN: 9780521864671 Application of algebraic geometry to statistical learning theory Algebraic geometry has also recently found applications to statistical learning theory, including a generalization of the Akaike information criterion to singular statistical models.

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