1. 使用libsvm工具箱时，可以指定使用工具箱自带的一些核函数（-t参数），主要有：
-t kernel_type : set type of kernel function (default 2)
2. 有时我们需要使用自己的核函数，这时候可以用 -t 4参数来实现：
-t kernel_type : set type of kernel function (default 2)
4 -- precomputed kernel (kernel values in training_instance_matrix)
使用-t 4参数时，再有了核函数后，需要给出核矩阵，关于核函数以及核函数构造相关的知识，大家可以看看相关书籍，在此不特别深入说明。
比如线性核函数 是 K(x,x‘) = (x * x‘)，设训练集是train_data，设训练集有150个样本 , 测试集是test_data，设测试集有120个样本
则 训练集的核矩阵是 ktrain1 = train_data*train_data‘
测试集的核矩阵是 ktest1 = test_data*train_data‘
想要使用-t 4参数还需要把样本的序列号放在核矩阵前面 ，形成一个新的矩阵，然后使用svmtrain建立支持向量机，再使用svmpredict进行预测即可。形式与使用其他-t参数少有不同，如下：
ktrain1 = train_data*train_data‘; Ktrain1 = [(1:150)‘,ktrain1]; model_precomputed1 = svmtrain(train_label, Ktrain1, ‘-t 4‘); % 注意此处的 输入 Ktrain1 ktest1 = test_data*train_data‘; Ktest1 = [(1:120)‘, ktest1]; [predict_label_P1, accuracy_P1, dec_values_P1] = svmpredict(test_label,Ktest1,model_precomputed1); % 注意此处输入Ktest1</pre>
下面是一个整体的小例子，大家可以看一下：
%% Use_precomputed_kernelForLibsvm_example % faruto % last modified by 2011.04.20 %% tic; clear; clc; close all; format compact; %% load heart_scale.mat; % Split Data train_data = heart_scale_inst(1:150,:); train_label = heart_scale_label(1:150,:); test_data = heart_scale_inst(151:270,:); test_label = heart_scale_label(151:270,:); %% Linear Kernel model_linear = svmtrain(train_label, train_data, ‘-t 0‘); [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear); %% Precomputed Kernel One % 使用的核函数 K(x,x‘) = (x * x‘) % 核矩阵 ktrain1 = train_data*train_data‘; Ktrain1 = [(1:150)‘,ktrain1]; model_precomputed1 = svmtrain(train_label, Ktrain1, ‘-t 4‘); ktest1 = test_data*train_data‘; Ktest1 = [(1:120)‘, ktest1]; [predict_label_P1, accuracy_P1, dec_values_P1] = svmpredict(test_label, Ktest1, model_precomputed1); %% Precomputed Kernel Two % 使用的核函数 K(x,x‘) = ||x|| * ||x‘|| % 核矩阵 ktrain2 = ones(150,150); for i = 1:150 for j = 1:150 ktrain2(i,j) = sum(train_data(i,:).^2)^0.5 * sum(train_data(j,:).^2)^0.5; end end Ktrain2 = [(1:150)‘,ktrain2]; model_precomputed2 = svmtrain(train_label, Ktrain2, ‘-t 4‘); ktest2 = ones(120,150); for i = 1:120 for j = 1:150 ktest2(i,j) = sum(test_data(i,:).^2)^0.5 * sum(train_data(j,:).^2)^0.5; end end Ktest2 = [(1:120)‘, ktest2]; [predict_label_P2, accuracy_P2, dec_values_P2] = svmpredict(test_label, Ktest2, model_precomputed2); %% Precomputed Kernel Three % 使用的核函数 K(x,x‘) = (x * x‘) / ||x|| * ||x‘|| % 核矩阵 ktrain3 = ones(150,150); for i = 1:150 for j = 1:150 ktrain3(i,j) = ... train_data(i,:)*train_data(j,:)‘/(sum(train_data(i,:).^2)^0.5 * sum(train_data(j,:).^2)^0.5); end end Ktrain3 = [(1:150)‘,ktrain3]; model_precomputed3 = svmtrain(train_label, Ktrain3, ‘-t 4‘); ktest3 = ones(120,150); for i = 1:120 for j = 1:150 ktest3(i,j) = ... test_data(i,:)*train_data(j,:)‘/(sum(test_data(i,:).^2)^0.5 * sum(train_data(j,:).^2)^0.5); end end Ktest3 = [(1:120)‘, ktest3]; [predict_label_P3, accuracy_P3, dec_values_P3] = svmpredict(test_label, Ktest3, model_precomputed3); %% Display the accuracy accuracyL = accuracy_L(1) % Display the accuracy using linear kernel accuracyP1 = accuracy_P1(1) % Display the accuracy using precomputed kernel One accuracyP2 = accuracy_P2(1) % Display the accuracy using precomputed kernel Two accuracyP3 = accuracy_P3(1) % Display the accuracy using precomputed kernel Three %% toc;
运行结果：
Accuracy = 85% (102/120) (classification) Accuracy = 85% (102/120) (classification) Accuracy = 67.5% (81/120) (classification) Accuracy = 84.1667% (101/120) (classification) accuracyL = 85 accuracyP1 = 85 accuracyP2 = 67.5000 accuracyP3 = 84.1667 Elapsed time is 1.424549 seconds.
原文：http://www.cnblogs.com/tec-vegetables/p/4506959.html