Towards robust model selection using estimation and approximation error bounds

Joel Ratsaby; Ronny Meir; Vitaly Maiorov

doi:10.1145/238061.238069

Towards robust model selection using estimation and approximation error bounds

Joel Ratsaby, Ronny Meir, Vitaly Maiorov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

One of the main problems in machine learning and statistical inference is selecting an appropriate model by which a set of data can be explained. A novel model selection criterion based on the uniform convergence of empirical processes combined with the results concerning the approximation ability of non-linear manifolds of functions is introduced. A coherent and robust framework for model selection was elucidated and a lower bound on the approximation error was established, giving a well specified sense for most functions of interest.

Original language	English
Title of host publication	Proceedings of the Annual ACM Conference on Computational Learning TheoryPages 57 - 671996 Proceedings of the 1996 9th Annual Conference on Computational Learning Theory28 June 1996through 1 July 1996
Pages	57-67
Number of pages	11
DOIs	https://doi.org/10.1145/238061.238069
State	Published - 1996
Externally published	Yes
Event	Proceedings of the 1996 9th Annual Conference on Computational Learning Theory - Desenzano del Garda, Italy Duration: 28 Jun 1996 → 1 Jul 1996

Conference

Conference	Proceedings of the 1996 9th Annual Conference on Computational Learning Theory
City	Desenzano del Garda, Italy
Period	28/06/96 → 1/07/96

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10.1145/238061.238069

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Ratsaby, J., Meir, R., & Maiorov, V. (1996). Towards robust model selection using estimation and approximation error bounds. In Proceedings of the Annual ACM Conference on Computational Learning TheoryPages 57 - 671996 Proceedings of the 1996 9th Annual Conference on Computational Learning Theory28 June 1996through 1 July 1996 (pp. 57-67) https://doi.org/10.1145/238061.238069

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title = "Towards robust model selection using estimation and approximation error bounds",

abstract = "One of the main problems in machine learning and statistical inference is selecting an appropriate model by which a set of data can be explained. A novel model selection criterion based on the uniform convergence of empirical processes combined with the results concerning the approximation ability of non-linear manifolds of functions is introduced. A coherent and robust framework for model selection was elucidated and a lower bound on the approximation error was established, giving a well specified sense for most functions of interest.",

author = "Joel Ratsaby and Ronny Meir and Vitaly Maiorov",

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doi = "10.1145/238061.238069",

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booktitle = "Proceedings of the Annual ACM Conference on Computational Learning TheoryPages 57 - 671996 Proceedings of the 1996 9th Annual Conference on Computational Learning Theory28 June 1996through 1 July 1996",

note = "Proceedings of the 1996 9th Annual Conference on Computational Learning Theory ; Conference date: 28-06-1996 Through 01-07-1996",

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Ratsaby, J, Meir, R & Maiorov, V 1996, Towards robust model selection using estimation and approximation error bounds. in Proceedings of the Annual ACM Conference on Computational Learning TheoryPages 57 - 671996 Proceedings of the 1996 9th Annual Conference on Computational Learning Theory28 June 1996through 1 July 1996. pp. 57-67, Proceedings of the 1996 9th Annual Conference on Computational Learning Theory, Desenzano del Garda, Italy, 28/06/96. https://doi.org/10.1145/238061.238069

Towards robust model selection using estimation and approximation error bounds. / Ratsaby, Joel; Meir, Ronny; Maiorov, Vitaly.
Proceedings of the Annual ACM Conference on Computational Learning TheoryPages 57 - 671996 Proceedings of the 1996 9th Annual Conference on Computational Learning Theory28 June 1996through 1 July 1996. 1996. p. 57-67.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Towards robust model selection using estimation and approximation error bounds

Abstract

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