使用libsvm工具箱，README文档要仔细阅读

       一直修改程序，虽然svmpredict用训练数据做出来没问题，但是把训练数据进行特征提取后，用svmpredict预测出来完全错的离谱，搞了一整天，最后终于还是用READEME文档解决了我的问题，核心就是：svmpredict里面['libsvm_options']参数的选择。所以README文档要仔细阅读。特此将其复制如下，方便以后查阅。

-----------------------------------------
--- MATLAB/OCTAVE interface of LIBSVM ---
-----------------------------------------


Table of Contents
=================


- Introduction
- Installation
- Usage
- Returned Model Structure
- Other Utilities
- Examples
- Additional Information




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 are 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


To use Octave, type 'make octave':


Example:
linux> make octave


On Windows systems, pre-built binary files are already in the directory 
`..\windows', so no need to conduct installation. Now we include both
32bit binary files and 64bit binary files, but in future releases, we 
will provide binary files only for 64bit MATLAB on Windows. 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 7.1 in C:\Program Files\Microsoft Visual Studio 
        [0] None 
        Compiler: 1
        Please verify your choices: 
        Compiler: Microsoft Visual C/C++ 7.1 
        Location: C:\Program Files\Microsoft Visual Studio 
        Are these correct?([y]/n): y


        matlab> make




For list of supported/compatible compilers for MATLAB, please check the
following page:


http://www.mathworks.com/support/compilers/current_release/


Usage
=====


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


        -training_label_vector:
            An m by 1 vector of training labels (type must be double).
        -training_instance_matrix:
            An m by n matrix of m training instances with n features.
            It can be dense or sparse (type must be double).
        -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. (type must be double)
        -testing_instance_matrix:
            An m by n matrix of m testing instances with n features.
            It can be dense or sparse. (type must be double)
        -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.


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


A matlab function libsvmread reads files in LIBSVM format: 


[label_vector, instance_matrix] = libsvmread('data.txt'); 


Two outputs are labels and instances, which can then be used as inputs
of svmtrain or svmpredict. 


A matlab function libsvmwrite writes Matlab matrix to a file in LIBSVM format:


libsvmwrite('data.txt', label_vector, instance_matrix]


The instance_matrix must be a sparse matrix. (type must be double)
For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready 
in the directory `..\windows', but in future releases, we will only 
include 64bit MATLAB binary files.


These codes are prepared by Rong-En Fan and Kai-Wei Chang from National
Taiwan University.


Examples
========


Train and test on the provided data heart_scale:


matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data


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


matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');


To use precomputed kernel, you must include sample serial number as
the first column of the training and testing data (assume your kernel
matrix is K, # of instances is n):


matlab> K1 = [(1:n)', K]; % include sample serial number as first column
matlab> model = svmtrain(label_vector, K1, '-t 4');
matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data


We give the following detailed example by splitting heart_scale into
150 training and 120 testing data.  Constructing a linear kernel
matrix and then using the precomputed kernel gives exactly the same
testing error as using the LIBSVM built-in linear kernel.


matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab>
matlab> % Split Data
matlab> train_data = heart_scale_inst(1:150,:);
matlab> train_label = heart_scale_label(1:150,:);
matlab> test_data = heart_scale_inst(151:270,:);
matlab> test_label = heart_scale_label(151:270,:);
matlab>
matlab> % Linear Kernel
matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
matlab>
matlab> % Precomputed Kernel
matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
matlab>
matlab> accuracy_L % Display the accuracy using linear kernel
matlab> accuracy_P % Display the accuracy using precomputed kernel


Note that for testing, you can put anything in the
testing_label_vector.  For more details of precomputed kernels, please
read the section ``Precomputed Kernels'' in the README of the LIBSVM
package.


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 and Ting-Fan Wu. 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]>,
or check the FAQ page:


http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q9:_MATLAB_interface