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Statistical Learning Theory Stochastic by Catoni Olivier (23 results)
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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)
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 5 out of 5 stars
paperback. Condition: Very Good.
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Condition: New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
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£ 32.91
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Add to basketSoftcover. 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.
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Statistical Learning Theory and Stochastic Optimization (Paperback)
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2004
ISBN 10: 3540225722 ISBN 13: 9783540225720
Language: English
Paperback. Condition: new. Paperback. 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. Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Statistical Learning Theory and Stochastic Optimization: Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 45.62
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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)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 49.13
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Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Probabilites De Saint-flour Xxxi - 2001
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 49.12
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Condition: New. pp. 292.
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Statistical Learning Theory and Stochastic Optimization
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2004, 2004
ISBN 10: 3540225722 ISBN 13: 9783540225720
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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£ 47.94
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Add to basketTaschenbuch. Condition: 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.
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Statistical Learning Theory and Stochastic Optimization : Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001
Published by Springer Berlin Heidelberg, 2004
ISBN 10: 3540225722 ISBN 13: 9783540225720
Language: English
£ 47.94
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Add to basketTaschenbuch. Condition: 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.
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paperback. Condition: New. In shrink wrap. Looks like an interesting title!
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Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Probabilites De Saint-flour Xxxi - 2001
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Add to basketPaperback. Condition: Like New. Like New. book.
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Statistical Learning Theory and Stochastic Optimization (Paperback)
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2004
ISBN 10: 3540225722 ISBN 13: 9783540225720
Language: English
£ 101.16
Convert currency£ 27.52 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. 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. Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Statistical Learning Theory and Stochastic Optimization
Published by Springer Berlin Heidelberg Aug 2004, 2004
ISBN 10: 3540225722 ISBN 13: 9783540225720
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 47.94
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Add to basketTaschenbuch. Condition: 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.
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£ 67.56
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Add to basketCondition: New. Print on Demand pp. 292 Illus.
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Condition: New. PRINT ON DEMAND pp. 292.
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Statistical Learning Theory and Stochastic Optimization
Published by Springer Berlin Heidelberg, 2004
ISBN 10: 3540225722 ISBN 13: 9783540225720
Language: English
£ 43.35
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Add to basketKartoniert / Broschiert. Condition: 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.
