Model Selection and Error Estimation in a Nutshell

Luca Oneto

ISBN 10: 3030243613 / ISBN 13: 9783030243616

Editore: Springer, Springer Aug 2020, 2020

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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.

Codice articolo 9783030243616

Dati bibliografici

• Titolo: Model Selection and Error Estimation in a Nutshell
• Editore: Springer, Springer Aug 2020
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030243613
• ISBN 13: 9783030243616
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 9 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 9 millimetri di profondità
• Peso: 236 grammi

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Riassunto

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.

Informazioni sull?autore

Luca Oneto was born in Rapallo, Italy in 1986. He received his BSc and MSc in Electronic Engineering at the University of Genoa, Italy respectively in 2008 and 2010. In 2014 he received his PhD from the same university in the School of Sciences and Technologies for Knowledge and Information Retrieval with the thesis ``Learning Based On Empirical Data''. In 2017 he obtained the Italian National Scientific Qualification for the role of Associate Professor in Computer Engineering and in 2018 he obtained the one in Computer Science. He worked as Assistant Professor in Computer Engineering at University of Genoa from 2016 to 2019. In 2018 he was co-founder of the spin-off ZenaByte s.r.l. He is currently Associate Professor in Computer Science at University of Pisa with particular interests in Statistical Learning Theory and Data Science. Besides being an editorial board member of the book series Modeling and Optimization in Science and Technologies he is also co-author of the textbook Introduction to Digital Systems Design (Donzellini et al., Springer, 2019).