Model selection and information criterion

Noboru Murata, Hyeyoung Park

研究成果: Chapter

2 被引用数 (Scopus)

抄録

In this chapter, a problem of estimating model parameters from observed data is considered such as regression and function approximation, and a method of evaluating the goodness of model is introduced. Starting from so-called leave-one-out cross-validation, and investigating asymptotic statistical properties of estimated parameters, a generalized Akaike's information criterion (AIC) is derived for selecting an appropriate model from several candidates. In addition to model selection, the concept of information criteria provides an assessment of the goodness of model in various situations. Finally, an optimization method using regularization is presented as an example.

本文言語English
ホスト出版物のタイトルInformation Theory and Statistical Learning
出版社Springer US
ページ333-354
ページ数22
ISBN(印刷版)9780387848150
DOI
出版ステータスPublished - 2009 12 1

ASJC Scopus subject areas

  • Computer Science(all)

引用スタイル