readme.bsvm

留一模型选择法leave-one-out model selection

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           COPYRIGHT NOTIFICATION

BSVM can be freely used for research purpose.
Use for commercial purposes is expressly prohibited
without contacting the authors.
BSVM is provided "as is" without express or implied warranty.

Chih-Wei Hsu and Chih-Jen Lin
Department of Computer Science and
Information Engineering
National Taiwan University 
Taipei 106, Taiwan
e-mail: [e-mail]

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           INSTRUCTIONS

<installation of BSVM, ommited>

3. Use -h to know options of bsvm
	
	bsvm -h		

4. Run the sample problems by executing bsvm. A model 
   file is generated

	bsvm -t 2 -v 1 -c 1000  heart_scale heart_model

5. Test the classifier

	classify heart_scale heart_model classfied_result
 
   test data: heart_scale
   support vectors: heart_model
   classified_result: results after classification 

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           FILE FORMATS and PARAMETERS

The format of training and testing data file is:

<label> <index1>:<value1> <index2>:<value2> ...
.
.
.

<label> is +1 or -1, <index> is an integer starting from 1, <value> is
a real number. The labels in the testing data file are only used to
calculate accuracy. If they are unknown, just fill this column with
+1.


Type bsvm to know the usage of bsvm:

Usage: bsvm [options] training_set_file [model_file]
options:
-h : this help
-m cachesize : set cache memory size in MB (default 160)
-q qpsize : set subproblem size (default 10)
-c cost : set cost C of constraints violation (default 1)
-e epsilon : set tolerance of termination criterion (default 0.001)
-v verbosity level T : output optimization messages every T iterations,
   0 means no messages during optimization (default 1)
-t kernel_type : set type of kernel function (default 2)
        0 -- linear
        1 -- polynomial: (gamma*u'*v + coef0)^degree
        2 -- radial basis function: exp(-gamma*|u-v|^2)
        3 -- sigmoid: tanh(gamma*u'*v + coef0)
-g gamma : set gamma in kernel function (default 1/k)
-d degree : set degree in kernel function (default 3)
-r coef0 : set coef0 in kernel function (default 0)


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           ADDITIONAL INFORMATION

Chih-Wei Hsu and Chih-Jen Lin
A simple decomposition method for  support vector machines
http://www.csie.ntu.edu.tw/~cjlin/papers/decomp.ps.gz

Acknowledgments:
The authors thank Chih-Chung Chang
for many helpful discussions and comments.
Part of the software implementation
also benefited from his help.


BSVM uses a modified implementation of the optimization solver TRON.
Many aspects specific to SVM are considered. 
TRON was originally written by Chih-Jen Lin and Jorge More'
and is available from 
http://www.mcs.anl.gov/~more/tron