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有关支持向量机的一些用于测试和分类的MATLAB程序 希望对大家有用

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--- Document for MATLAB interface of LIBSVM ---
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Introduction
============

This tool provides a simple interface to LIBSVM, a library for support vector
machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as
the usage and the way of specifying parameters is the same as that of LIBSVM.

Installation
============

On Unix systems, we recommend using GNU g++ as your compiler and
type 'make' to build 'svmtrain.mexglx' and 'svmpredict.mexglx'.
Note that we assume your MATLAB is installed in '/usr/local/matlab',
if not, please change MATLABDIR in Makefile.

Example:
        linux> make

On Windows systems, pre-built 'svmtrain.dll' and 'svmpredict.dll' are
included in this package, so no need to conduct installation. If you
have modified the sources and would like to re-build the package, type
'mex -setup' in MATLAB to choose a compiler for mex first. Then type
'make' to start the installation.

Example:
        matlab> mex -setup
        (ps: MATLAB will show the following messages to setup default compiler.)
        Please choose your compiler for building external interface (MEX) files: 
        Would you like mex to locate installed compilers [y]/n? y
        Select a compiler: 
        [1] Microsoft Visual C/C++ version 6.0 in C:\Program Files\Microsoft Visual Studio 
        [0] None 
        Compiler: 1
        Please verify your choices: 
        Compiler: Microsoft Visual C/C++ 6.0 
        Location: C:\Program Files\Microsoft Visual Studio 
        Are these correct?([y]/n): y

        matlab> make

Usage
=====

matlab> model = svmtrain(training_label_vector, training_instance_matrix, [,'libsvm_options']);

        -training_label_vector:
            An m by 1 vector of training labels.
        -training_instance_matrix:
            An m by n matrix of m training instances with n features.
            It can be dense or sparse.
        -libsvm_options:
            A string of training options in the same format as that of LIBSVM.

matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [,'libsvm_options']);

        -testing_label_vector:
            An m by 1 vector of prediction labels. If labels of test
            data are unknown, simply use any random values.
        -testing_instance_matrix:
            An m by n matrix of m testing instances with n features.
            It can be dense or sparse.
        -model:
            The output of svmtrain.
        -libsvm_options:
            A string of testing options in the same format as that of LIBSVM.

Returned Model Structure
========================

The 'svmtrain' function returns a model which can be used for future
prediction.  It is a structure and is organized as [Parameters, nr_class,
totalSV, rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]:

        -Parameters: parameters
        -nr_class: number of classes; = 2 for regression/one-class svm
        -totalSV: total #SV
        -rho: -b of the decision function(s) wx+b
        -Label: label of each class; empty for regression/one-class SVM
        -ProbA: pairwise probability information; empty if -b 0 or in one-class SVM
        -ProbB: pairwise probability information; empty if -b 0 or in one-class SVM
        -nSV: number of SVs for each class; empty for regression/one-class SVM
        -sv_coef: coefficients for SVs in decision functions
        -SVs: support vectors

If you do not use the option '-b 1', ProbA and ProbB are empty
matrices. If the '-v' option is specified, cross validation is
conducted and the returned model is just a scalar: cross-validation
accuracy for classification and mean-squared error for regression.

More details about this model can be found in LIBSVM FAQ
(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVM
implementation document
(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).

Result of Prediction
====================

The function 'svmpredict' has three outputs. The first one,
predictd_label, is a vector of predicted labels. The second output,
accuracy, is a vector including accuracy (for classification), mean
squared error, and squared correlation coefficient (for regression).
The third is a matrix containing decision values or probability
estimates (if '-b 1' is specified). If k is the number of classes, for decision values, 
each row includes results of predicting k(k-1/2) binary-class SVMs. For probabilities, 
each row contains k values indicating the probability that the testing instance is in
each class. Note that the order of classes here is the same as 'Label'
field in the model structure.

Examples
========

matlab> load heart_scale.mat
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 2');
matlab> [predict_label, accuracy] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data

For probability estimates, you need '-b 1' for training and testing:

matlab> load heart_scale.mat
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 2 -b 1');
matlab> load heart_scale.mat
matlab> [predict_label, accuracy] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');

Other Utilities
===============

A simple matlab program read_sparse.m reads files in libsvm format: 

[svm_lbl, svm_data] = read_sparse(fname); 

Two outputs are labels and instances, which can then be used as inputs
of svmtrain or svmpredict. This code was initiated by Hsuan-Tien Lin
from Caltech and rewritten by Rong-En Fan from National Taiwan
University.

Additional Information
======================

This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,
Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer
Science, National Taiwan University. The current version was prepared
by Rong-En Fan. If you find this tool useful, please cite LIBSVM as
follows

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for
support vector machines, 2001. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm

For any question, please contact Chih-Jen Lin <[e-mail]>.