New Support Vector Algorithms

Schölkopf, Bernhard; Smola, Alex J.; Williamson, Robert C.; Bartlett, Peter L.

doi:10.1162/089976600300015565

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New Support Vector Algorithms

Author(s): Bernhard Schölkopf , Alex J. Smola , Robert C. Williamson , Peter L. Bartlett

Publication date Created: May 2000

Publication date (Print): May 2000

Journal: Neural Computation

Publisher: MIT Press - Journals

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Comparing support vector machines with Gaussian kernels to radial basis function classifiers

B Schölkopf, Kah-Kay Sung, C.J.C. Burges … (1997)

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The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

P.L. Bartlett (1998)

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Network information criterion-determining the number of hidden units for an artificial neural network model.

N Murata, S Yoshizawa, S Amari (1994)

The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.

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Journal

Title: Neural Computation

Abbreviated Title: Neural Computation

Publisher: MIT Press - Journals

ISSN (Print): 0899-7667

ISSN (Electronic): 1530-888X

Publication date Created: May 2000

Publication date (Print): May 2000

Volume: 12

Issue: 5

Pages: 1207-1245

Article

DOI: 10.1162/089976600300015565

SO-VID: 7e4928d4-510f-44a1-ab69-a37bbba7b959

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New Support Vector Algorithms

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Most cited references 8

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Network information criterion-determining the number of hidden units for an artificial neural network model.

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