A New idea on Support Vector Machine

[Si Wu]

Dear Connectionists,

This is to announce our new idea on support vector machines,

  "Improving Support Vector Machine Classifiers by Modifying
   Kernel Functions".

We remarked that there exist few theories concerning how to choose
a kernel function to fit given data well. This is equivalent to
how to choose a smoothing operator. This is a difficult question.
We used an idea of conformal transformation given by information
geometry to modify a given kernel function. We hope that this 
gives a new direction for further development of SVMs. The paper
will appear in Neural Networks as a Letter. If you are interested
in, you can freely download it from 

   http://www.islab.brain.riken.go.jp/~phwusi/Publication.html

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Abstract

In this work, we propose a method of modifying a kernel function in
a data-dependent way to improve the performance of a support vector
machine classifier. This is based on the Riemannian geometrical
structure induced by the kernel function. The idea is to enlarge
the spatial resolution around the separating boundary surface by
a conformal mapping such that the separability between classes is
increased. Examples are given specifically for modifying Gaussian
Radial Basis Function kernels. Simulation results for both artificial
and real data show remarkable improvement of generalization errors, 
supporting our idea.


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|                                                                |
|  Tel: 0081-48-462-4267(H), 0081-48-467-9664(O)                 |
|  E-mail: phwusi at islab.brain.riken.go.jp                        |
|  http://www.islab.brain.riken.go.jp/~phwusi                    |
|  Lab. for Information Synthesis, RIKEN Brain Science Institute |
|  Hirosawa 2-1, Wako-shi, Saitama 351-01, JAPAN                 |
|________________________________________________________________|