Model Selection and Error Estimation in a Nutshell by Luca Oneto
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.-
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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).
| SKU | Unavailable |
| ISBN 13 | 9783030243616 |
| ISBN 10 | 3030243613 |
| Title | Model Selection and Error Estimation in a Nutshell |
| Author | Luca Oneto |
| Series | Modeling And Optimization In Science And Technologies |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2020-08-14 |
| Number of pages | 132 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |