Model Selection and Error Estimation in a Nutshell

Language: English

Published by Springer, Springer Jul 2019, 2019

3030243583 / 9783030243586

Series: Book 11 of 14 - Modeling and Optimization in Science and Technologies

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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.

Seller Inventory # 9783030243586

Title
Model Selection and Error Estimation in a Nutshell
Author
Luca Oneto
Publisher
Springer, Springer Jul 2019
Publication year
2019
Condition
Neu
Binding
Buch
Language
English
ISBN 10
3030243583
ISBN 13
9783030243586
Item weight
395 grams
Dimensions
241x160x14 mm
Series
Book 11 of 14: Modeling and Optimization in Science and Technologies

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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AbeBooks seller since January 23, 2017

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