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Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.
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Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.
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Statistical Learning Theory and Stochastic Optimization.
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Berlin, Springer, 2004
ISBN 10: 3540225722
ISBN 13: 9783540225720
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Librería: Antiquariat Bookfarm, Löbnitz, Alemania
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Softcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. C-04205 9783540225720 Sprache: Englisch Gewicht in Gramm: 550. Nº de ref. del artículo: 2490434
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Statistical Learning Theory and Stochastic Optimization: Ecole d'Età de Probabilitàs de Saint-Flour XXXI- 2001 (Lecture Notes in Mathematics, Vol. 1851) (Lecture Notes in Mathematics, 1851)
Librería: Books From California, Simi Valley, CA, Estados Unidos de America
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paperback. Condición: Very Good. Nº de ref. del artículo: mon0003673422
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Statistical Learning Theory and Stochastic Optimization: Ecole d'Eté de Probabilités de Saint-Flour XXXI- 2001 (Lecture Notes in Mathematics, Vol. 1851) (Lecture Notes in Mathematics, 1851)
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Statistical Learning Theory and Stochastic Optimization
Publicado por
Springer Berlin Heidelberg Aug 2004, 2004
ISBN 10: 3540225722
ISBN 13: 9783540225720
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Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use asis often done in practice a notoriously 'wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools,that will stimulate further studies and results. 292 pp. Englisch. Nº de ref. del artículo: 9783540225720
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Statistical Learning Theory and Stochastic Optimization : Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001
Publicado por
Springer Berlin Heidelberg, 2004
ISBN 10: 3540225722
ISBN 13: 9783540225720
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use asis often done in practice a notoriously 'wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools,that will stimulate further studies and results. Nº de ref. del artículo: 9783540225720
Cantidad disponible: 1 disponibles
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Statistical Learning Theory and Stochastic Optimization: Ecole d'Eté de Probabilités de Saint-Flour XXXI- 2001 (Lecture Notes in Mathematics, Vol. 1851) (Lecture Notes in Mathematics, 1851)
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Statistical Learning Theory and Stochastic Optimization
Publicado por
Springer Berlin Heidelberg, 2004
ISBN 10: 3540225722
ISBN 13: 9783540225720
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Kartoniert / Broschiert
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it ma. Nº de ref. del artículo: 4885609
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Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Probabilites De Saint-flour Xxxi - 2001
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Statistical Learning Theory and Stochastic Optimization : Ecole d'Ete de Probabilites de Saint-Flour XXXI - 2001
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PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9783540225720
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Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Probabilites De Saint-flour Xxxi - 2001
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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