Model Selection Error Estimation by Oneto Luca (22 results)
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Published by Springer 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Published by Springer Nature 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Taschenbuch. Condition: Neu. Model Selection and Error Estimation in a Nutshell | Luca Oneto | Taschenbuch | Modeling and Optimization in Science and Technologies | xiii | Englisch | 2020 | Springer | EAN 9783030243616 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]har…tmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer International Publishing 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words…, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80's and includes the most recent results. It discusses open problems and outlines future directions for research.
Language: English
Published by Springer International Publishing 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in othe…r words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80's and includes the most recent results. It discusses open problems and outlines future directions for research.
Language: English
Published by Springer Nature Switzerland AG 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Language: English
Published by Springer 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Language: English
Published by Springer International Publishing Aug 2020 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a m…odel or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80's and includes the most recent results. It discusses open problems and outlines future directions for research. 148 pp. Englisch.
Language: English
Published by Springer International Publishing Jul 2019 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or…, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80's and includes the most recent results. It discusses open problems and outlines future directions for research. 148 pp. Englisch.
Language: English
Published by Springer International Publishing 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reviews the main approaches to problems of model selection and error estimation Simplifies most of the technical aspects focusing on the applicability of the approachesPresents the intuitions behind the methods, the f…ormalism, and practical al.
Language: English
Published by Springer International Publishing 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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- Print on Demand
Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reviews the main approaches to problems of model selection and error estimation Simplifies most of the technical aspects focusing on the applicability of the approachesPresents the intuitions behind the methods, the f…ormalism, and practical al.
Language: English
Published by Springer 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Condition: New. Print on Demand pp. 132.
Language: English
Published by Springer 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Published by Springer 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Condition: New. PRINT ON DEMAND pp. 132.
Language: English
Published by Springer 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Published by Springer, Springer Jul 2019 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in… other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80¿s and includes the most recent results. It discusses open problems and outlines future directions for research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 148 pp. Englisch.
Language: English
Published by Springer 2019
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
- Hardcover
- Print on Demand
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Buch. Condition: Neu. Model Selection and Error Estimation in a Nutshell | Luca Oneto | Buch | Modeling and Optimization in Science and Technologies | xiii | Englisch | 2019 | Springer | EAN 9783030243586 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]sprin…ger[dot]com | Anbieter: preigu Print on Demand.
Language: English
Published by Springer, Springer Aug 2020 2020
Series: Modeling and Optimization in Science and Technologies, Book 11 of 14. Book 11 of 14 - Modeling and Optimization in Science and Technologies
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -How can we select the best performing data-driven model How can we rigorously estimate its generalization error Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model… or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80¿s and includes the most recent results. It discusses open problems and outlines future directions for research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 148 pp. Englisch.
