model selection software for support vector machines

[Chih-Jen Lin]

Dear Colleagues:

We announce the release of the software looms, a 
leave-one-out model selection software for 
support vector machines (SVM).

Automatic model selection is an important
issue to make support vector machines
(SVM) practically useful. 
Most existing approaches use the leave-one-out (loo)
related estimators which are considered computationally 
expensive. looms uses some numerical tricks which 
lead to efficient calculation of loo rates of different models.

Given a range of parameters, looms automatically returns the 
parameter and model with the best loo rate. For example,

% looms heart_scale
Optimal parameter: c=16.000000, gamma=0.016000, rate= 83.704%

where c is the penalty parameter (or say the upper
bound of the SVM dual formulation) and
gamma is the parameter of the RBF kernel:
exp(gamma*|x_i - x_j|^2). Currently we 
support only the RBF kernel.

The current release (Version 1.0, by Jen-Hao Lee and Chih-Jen Lin)
 is available from

  http://www.csie.ntu.edu.tw/~cjlin/looms

Details of looms are in the following paper:
J.-H. Lee and C.-J. Lin, 
Automatic model selection for support vector machines
http://www.csie.ntu.edu.tw/~cjlin/papers/modelselect.ps.gz

Any comments are very welcome.

Sincerely,
Chih-Jen Lin
Department of Computer Science and
Information Engineering
National Taiwan University 
Taipei, Taiwan
cjlin at csie.ntu.edu.tw