Model Selection and Error Estimation in a Nutshell / / by Luca Oneto

• Autore: Oneto Luca
• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (135 pages)
• Disciplina: 006.31
• Soggetto topico: Computational intelligence; Statistics ; Data mining; Computational Intelligence; Statistical Theory and Methods; Data Mining and Knowledge Discovery
• Nota di contenuto: Introduction -- The “Five W” of MS & EE -- Preliminaries -- Resampling Methods -- Complexity-Based Methods -- Compression Bound -- Algorithmic Stability Theory -- PAC-Bayes Theory -- Differential Privacy Theory -- Conclusions & Further Readings.
• Sommario/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.
• Titolo autorizzato: Model Selection and Error Estimation in a Nutshell
• ISBN: 3-030-24359-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483031303321
• Lo trovi qui: Univ. Federico II
• Serie: Modeling and Optimization in Science and Technologies, . 2196-7326 ; ; 15