Paper: Bayesian Support Vectors

[J.F. Gomes De Freitas]

Dear colleagues

A paper, to appear in NNSP99, on sequential Bayesian estimation 
techniques for support vectors is now available at:

http://svr-www.eng.cam.ac.uk/~jfgf/publications.html

As I am presently writing a longer version, I would very much appreciate 
your feedback, especially if you have any negative comments and/or if you 
can answer some of the questions I pose in the paper.

ABSTRACT:
In this paper, we derive an algorithm to train support vector machines
sequentially. The algorithm makes use of the Kalman filter and is
optimal in a Bayesian framework. It extends the support vector
machine paradigm to applications involving real-time and
non-stationary signal processing. It also provides a computationally
efficient alternative to the problem of quadratic optimisation.

Thanks
Nando

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JFG de Freitas (Nando)
Speech, Vision and Robotics Group
Information Engineering
Cambridge University
CB2 1PZ England
http://svr-www.eng.cam.ac.uk/~jfgf        
Tel (01223) 302323 (H)                 
    (01223) 332754 (W)
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