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Statistical Learning Theory and Stochastic Optimization: Ecole d'Et?? de Probabilit??s de Saint-Flour XXXI - 2001 - Couverture souple
Synopsis
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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- ÉditeurSpringer
- Date d'édition2008
- ISBN 10 3540225722
- ISBN 13 9783540225720
- ReliureBroché
- Langueanglais
- Nombre de pages292
- Coordonnées du fabricantnon disponible
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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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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)
Edité par
Springer, 2004
ISBN 10 : 3540225722
ISBN 13 : 9783540225720
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Statistical Learning Theory and Stochastic Optimization
Edité par
Springer Berlin Heidelberg, 2004
ISBN 10 : 3540225722
ISBN 13 : 9783540225720
Neuf
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Kartoniert / Broschiert. Etat : 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. N° de réf. du vendeur 4885609
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Statistical Learning Theory and Stochastic Optimization : Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001
Edité par
Springer Berlin Heidelberg, 2004
ISBN 10 : 3540225722
ISBN 13 : 9783540225720
Neuf
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783540225720
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Statistical Learning Theory and Stochastic Optimization
Edité par
Springer Berlin Heidelberg Aug 2004, 2004
ISBN 10 : 3540225722
ISBN 13 : 9783540225720
Neuf
Taschenbuch
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783540225720
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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 : Ecole d'Ete de Probabilites de Saint-Flour XXXI - 2001
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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
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2004, 2004
ISBN 10 : 3540225722
ISBN 13 : 9783540225720
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. 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 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch. N° de réf. du vendeur 9783540225720
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Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Probabilites De Saint-flour Xxxi - 2001
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