Model Selection and Error Estimation in a Nutshell

✍ Scribed by Luca Oneto

• Publisher: Springer International Publishing
• Year: 2020
• Tongue: English
• Leaves: 135
• Series: Modeling and Optimization in Science and Technologies 15
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xiii
Introduction (Luca Oneto)....Pages 1-3
The “Five W” of MS and EE (Luca Oneto)....Pages 5-11
Preliminaries (Luca Oneto)....Pages 13-23
Resampling Methods (Luca Oneto)....Pages 25-31
Complexity-Based Methods (Luca Oneto)....Pages 33-57
Compression Bound (Luca Oneto)....Pages 59-63
Algorithmic Stability Theory (Luca Oneto)....Pages 65-74
PAC-Bayes Theory (Luca Oneto)....Pages 75-86
Differential Privacy Theory (Luca Oneto)....Pages 87-97
Conclusions and Further Readings (Luca Oneto)....Pages 99-100
Back Matter ....Pages 101-132

✦ Subjects

Engineering; Computational Intelligence; Statistical Theory and Methods; Data Mining and Knowledge Discovery