Regularization in kernel learning

Shahar Mendelson; Joseph Neeman

doi:10.1214/09-AOS728

Regularization in kernel learning

Shahar Mendelson^*, Joseph Neeman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

90 Citations (Scopus)

Abstract

Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.

Original language	English
Pages (from-to)	526-565
Number of pages	40
Journal	Annals of Statistics
Volume	38
Issue number	1
DOIs	https://doi.org/10.1214/09-AOS728
Publication status	Published - Feb 2010

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title = "Regularization in kernel learning",

abstract = "Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.",

keywords = "Least-squares, Model selection, Regression, Regulation, Reproducing kernel Hilbert space",

author = "Shahar Mendelson and Joseph Neeman",

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AB - Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.

KW - Least-squares

KW - Model selection

KW - Regression

KW - Regulation

KW - Reproducing kernel Hilbert space

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U2 - 10.1214/09-AOS728

DO - 10.1214/09-AOS728

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JO - Annals of Statistics

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Regularization in kernel learning

Abstract

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