A Neural Support Vector Network Architecture with Adaptive Kernels

@inproceedings{Vincent2000ANS,
  title={A Neural Support Vector Network Architecture with Adaptive Kernels},
  author={Pascal Vincent and Yoshua Bengio},
  booktitle={IJCNN},
  year={2000}
}
In the Support Vector Machines (SVM) framework, the positiv e-definite kernel can be seen as representing a fixed similarity measure between two patterns, and a discrim inant function is obtained by taking a linear combination of the kernels computed at training examples called sup port vectors. Here we investigate learning architectures in which the kernel functions can be replaced by more general simi arity measures that can have arbitrary internal parameters. The training criterion used in… CONTINUE READING
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