Model Selection and Error Estimation in a Nutshell - Couverture rigide

Oneto, Luca

Couverture rigide

ISBN 10 : 3030243583 ISBN 13 : 9783030243586

Editeur : Springer Nature Switzerland AG, 2019

Synopsis

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.

A propos du livre

À propos de l?auteur

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).

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2019
• Langue: anglais
• ISBN 10: 3030243583
• ISBN 13: 9783030243586
• Reliure: Relié
• Nombre de pages: 145
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Allemagne