(Matlab)SVM工具箱快速入手简易教程

SVM工具箱快速入手简易教程(by faruto)
一. matlab 自带的函数(matlab帮助文件里的例
子)[只有较新版本的matlab中有这两个SVM的函数] =====
svmtrain svmclassify
=====简要语法规则====
svmtrain
Train support vector machine classifier
Syntax
SVMStruct = svmtrain(Training, Group)
SVMStruct = svmtrain(..., 'Kernel_Function', Kernel_FunctionValue, ...) SVMStruct = svmtrain(..., 'RBF_Sigma', RBFSigmaValue, ...)
SVMStruct = svmtrain(..., 'Polyorder', PolyorderValue, ...) SVMStruct = svmtrain(..., 'Mlp_Params', Mlp_ParamsValue, ...) SVMStruct = svmtrain(..., 'Method', MethodValue, ...)
SVMStruct = svmtrain(..., 'QuadProg_Opts', QuadProg_OptsValue, ...) SVMStruct = svmtrain(..., 'SMO_Opts', SMO_OptsValue, ...)
SVMStruct = svmtrain(..., 'BoxConstraint', BoxConstraintValue, ...) SVMStruct = svmtrain(..., 'Autoscale', AutoscaleValue, ...) SVMStruct = svmtrain(..., 'Showplot', ShowplotValue, ...)
---------------------
svmclassify
Classify data using support vector machine
Syntax
Group = svmclassify(SVMStruct, Sample)
Group = svmclassify(SVMStruct, Sample, 'Showplot', ShowplotValue)
============================实例研究====================
load fisheriris
%载入matlab自带的数据[有关数据的信息可以自己到UCI查找,这是UCI的经典数据之一],得到的数据如下图:
其中meas是150*4的矩阵代表着有150个样本每个样本有4个属性描述,species 代表着这150个样本的分类.
data = [meas(:,1), meas(:,2)];
%在这里只取meas的第一列和第二列,即只选取前两个属性.
groups = ismember(species,'setosa');
%由于species分类中是有三个分类:setosa,versicolor,virginica,为了使问题简单,我们将其变为二分类问题:Setosa and non-Setosa.
[train, test] = crossvalind('holdOut',groups);
cp = classperf(groups);
%随机选择训练集合测试集[有关crossvalind的使用请自己help一下]
其中cp作用是后来用来评价分类器的.
svmStruct = svmtrain(data(train,:),groups(train),'showplot',true);
%使用svmtrain进行训练,得到训练后的结构svmStruct,在预测时使用.
训练结果如图:
classes = svmclassify(svmStruct,data(test,:),'showplot',true); %对于未知的测试集进行分类预测,结果如图:
classperf(cp,classes,test);
cp.CorrectRate
ans =
0.9867
%分类器效果测评,就是看测试集分类的准确率的高低.
二.台湾林智仁的libsvm工具箱
该工具箱下载[libsvm-mat-2.86-1]: libsvm-mat-2.86-1.rar (73.75 KB)
安装方法也很简单,解压文件,把当前工作目录调整到libsvm所在的文件夹下,再在set path里将libsvm 所在的文件夹加到里面.然后
在命令行里输入
mex -setup %选择一下编译器
make
这样就可以了.
建议大家使用libsvm工具箱,这个更好用一些.可以进行分类[多类别],预测....
=========
svmtrain
svmpredict
================
简要语法:
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
========================
实例研究:
load heart_scale.mat
%工具箱里自带的数据
如图:
tu4
其中 heart_scale_inst是样本,heart_scale_label
是样本标签
model = svmtrain(heart_scale_label,
heart_scale_inst, '-c 1 -g 0.07');
%训练样本,具体参数的调整请看帮助文件
[predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model);
%分类预测,这里把训练集当作测试集,验证效果如下: >> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data
Accuracy = 86.6667% (234/270) (classification)
==============
这回把SVM这点入门的东西都说完了,大家可以参照着上手了,有关SVM的原理我下面有个简易的PPT,是以前做项目时我做的[当时我负责有关SVM这一块代码实现讲解什么的],感兴趣的你可以看看,都是上手较快的东西,想要深入学习SVM,你的学习统计学习理论什么的....挺多的呢..
SVM.ppt (391 KB)
-----------有关SVM和libsvm的非常好的资料,想要详细研究SVM看这个------
libsvm_guide.pdf (194.53 KB)
libsvm_library.pdf (316.82 KB) OptimizationSupportVectorMachinesandMachine。