Title :
A Neural Support Vector Network architecture with adaptive kernels
Author :
Vincent, Pascal ; Bengio, Yoshua
Author_Institution :
Dept. d´´Inf. et de Recherche Oper., Montreal Univ., Que., Canada
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. 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
Keywords :
learning (artificial intelligence); neural net architecture; pattern classification; Neural Support Vector Network; Support Vector Machines; adaptive kernels; discriminant function; experiments; learning architectures; pattern classification; positive-definite kernel; training examples; Adaptive systems; Computer architecture; Handwriting recognition; Kernel; Neural networks; Pattern classification; Pattern recognition; Performance evaluation; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861455