Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2000)

Como, Italy

July 24, 2000 to July 27, 2000

ISSN: 1098-7576

ISBN: 0-7695-0619-4

pp: 5187

Pascal Vincent , Universit? de Montr?al

Yoshua Bengio , Universit? de Montr?al

ABSTRACT

In the Support Vector Machines (SVM) framework, the positive-definite kernel can be seen as representing a fixed similarity measure between two patterns, and a discriminant function is obtained by taking a linear combination of the kernels computed at training examples called support vectors. Here we investigate learning architectures in which the kernel functions can be replaced by more general similarity measures that can have arbitrary internal parameters. The training criterion used in SVMs is not appropriate for this purpose so we adopt the simple criterion that is generally used when training neural networks for classification tasks. Several experiments are performed which show that such Neural Support Vector Networks perform similarly to SVMs while requiring significantly fewer support vectors, even when the similarity measure has no internal parameters.

INDEX TERMS

CITATION

P. Vincent and Y. Bengio, "A Neural Support Vector Network Architecture with Adaptive Kernels,"

*Neural Networks, IEEE - INNS - ENNS International Joint Conference on(IJCNN)*, Como, Italy, 2000, pp. 5187.

doi:10.1109/IJCNN.2000.861455

CITATIONS