1.软件版本

MATLAB2021a

2.核心代码

function [model] = svmTrain(X, Y, C, kernelFunction, ...tol, max_passes)if ~exist('tol', 'var') || isempty(tol)tol = 1e-3;
endif ~exist('max_passes', 'var') || isempty(max_passes)max_passes = 5;
end% Data parameters
m = size(X, 1);
n = size(X, 2);% Map 0 to -1
Y(Y==0) = -1;% Variables
alphas = zeros(m, 1);
b = 0;
E = zeros(m, 1);
passes = 0;
eta = 0;
L = 0;
H = 0;% Pre-compute the Kernel Matrix since our dataset is small
% (in practice, optimized SVM packages that handle large datasets
%  gracefully will _not_ do this)
%
% We have implemented optimized vectorized version of the Kernels here so
% that the svm training will run faster.
if strcmp(func2str(kernelFunction), 'linearKernel')% Vectorized computation for the Linear Kernel% This is equivalent to computing the kernel on every pair of examplesK = X*X';
elseif strfind(func2str(kernelFunction), 'gaussianKernel')% Vectorized RBF Kernel% This is equivalent to computing the kernel on every pair of examplesX2 = sum(X.^2, 2);K = bsxfun(@plus, X2, bsxfun(@plus, X2', - 2 * (X * X')));K = kernelFunction(1, 0) .^ K;
else% Pre-compute the Kernel Matrix% The following can be slow due to the lack of vectorizationK = zeros(m);for i = 1:mfor j = i:mK(i,j) = kernelFunction(X(i,:)', X(j,:)');K(j,i) = K(i,j); %the matrix is symmetricendend
end% Train
fprintf('\nTraining ...');
dots = 12;
while passes < max_passes,num_changed_alphas = 0;for i = 1:m,% Calculate Ei = f(x(i)) - y(i) using (2). % E(i) = b + sum (X(i, :) * (repmat(alphas.*Y,1,n).*X)') - Y(i);E(i) = b + sum (alphas.*Y.*K(:,i)) - Y(i);if ((Y(i)*E(i) < -tol && alphas(i) < C) || (Y(i)*E(i) > tol && alphas(i) > 0)),% In practice, there are many heuristics one can use to select% the i and j. In this simplified code, we select them randomly.j = ceil(m * rand());while j == i,  % Make sure i \neq jj = ceil(m * rand());end% Calculate Ej = f(x(j)) - y(j) using (2).E(j) = b + sum (alphas.*Y.*K(:,j)) - Y(j);% Save old alphasalpha_i_old = alphas(i);alpha_j_old = alphas(j);% Compute L and H by (10) or (11). if (Y(i) == Y(j)),L = max(0, alphas(j) + alphas(i) - C);H = min(C, alphas(j) + alphas(i));elseL = max(0, alphas(j) - alphas(i));H = min(C, C + alphas(j) - alphas(i));endif (L == H),% continue to next i. continue;end% Compute eta by (14).eta = 2 * K(i,j) - K(i,i) - K(j,j);if (eta >= 0),% continue to next i. continue;end% Compute and clip new value for alpha j using (12) and (15).alphas(j) = alphas(j) - (Y(j) * (E(i) - E(j))) / eta;% Clipalphas(j) = min (H, alphas(j));alphas(j) = max (L, alphas(j));% Check if change in alpha is significantif (abs(alphas(j) - alpha_j_old) < tol),% continue to next i. % replace anywayalphas(j) = alpha_j_old;continue;end% Determine value for alpha i using (16). alphas(i) = alphas(i) + Y(i)*Y(j)*(alpha_j_old - alphas(j));% Compute b1 and b2 using (17) and (18) respectively. b1 = b - E(i) ...- Y(i) * (alphas(i) - alpha_i_old) *  K(i,j)' ...- Y(j) * (alphas(j) - alpha_j_old) *  K(i,j)';b2 = b - E(j) ...- Y(i) * (alphas(i) - alpha_i_old) *  K(i,j)' ...- Y(j) * (alphas(j) - alpha_j_old) *  K(j,j)';% Compute b by (19). if (0 < alphas(i) && alphas(i) < C),b = b1;elseif (0 < alphas(j) && alphas(j) < C),b = b2;elseb = (b1+b2)/2;endnum_changed_alphas = num_changed_alphas + 1;endendif (num_changed_alphas == 0),passes = passes + 1;elsepasses = 0;endfprintf('.');dots = dots + 1;if dots > 78dots = 0;fprintf('\n');endif exist('OCTAVE_VERSION')fflush(stdout);end
end
fprintf(' Done! \n\n');% Save the model
idx = alphas > 0;
model.X= X(idx,:);
model.y= Y(idx);
model.kernelFunction = kernelFunction;
model.b= b;
model.alphas= alphas(idx);
model.w = ((alphas.*Y)'*X)';end

3.操作步骤与仿真结论

4.参考文献

[1] Osuna E , Freund R , Girosi F . Training svm: An application to face detection. 1997.

D224

5.完整源码获得方式

方式1：微信或者QQ联系博主

方式2：订阅MATLAB/FPGA教程，免费获得教程案例以及任意2份完整源码

【SVM】基于matlab的SVM支持向量机训练和测试仿真相关推荐

基于MATLAB的SVM的交通标志识别
基于MATLAB的SVM的交通标志识别摘要:本文针对三种不同的交通标识(直行.右拐和直行左拐)给出了一种基于SVM识别方法.该方法首先在分析训练集交通标识图片特点的基础上,提取它们的PHOG特征向量 ...
java数字图像处理开题报告,基于MATLAB的数字图像处理算法研究与仿真开题报告...
基于MATLAB的数字图像处理算法研究与仿真开题报告毕业设计 (2013 届) 题目基于 MATLAB 的数字图像处理算法研究与仿真学院物理电气信息学院专业通信工程年级 0 ...
基于Matlab使用激光雷达检测分类跟踪车辆仿真（附源码）
目录一.概述二.加载数据三.接地层分割四.语义分割五.簇状和边界框管接头六.可视化设置七.总结八.程序此示例演示如何使用安装在 ego 车辆上的激光雷达传感器捕获的激光雷达点云数据来 ...
如何使用matlab得出pid控制参数值,基于MATLAB的PID控制器参数整定及仿真
基于MATLAB的PID控制器参数整定及仿真摘要:PID控制器结构和算法简单,应用广泛,但参数整定比较复杂,在此我探讨利用MATLAB实现PID参数整定及其仿真的方法,并分析比较比例.比例积分.比例 ...
循环码差错图样matlab,基于MATLAB的(15,7)循环码的编译仿真.doc
基于MATLAB的(15,7)循环码的编译仿真 <纠错码与差错控制> 课程设计题目:基于MATLAB的(15,7)循环码的编译仿真院(系) 信息科学与工程学院专业通信工程专业届 ...
基于matlab的数字下变频器的设计与仿真应用,基于MATLAB的数字下变频器的设计与仿真应用.pdf...
基于MATLAB的数字下变频器的设计与仿真应用中国科技论文在线基于 Matlab 的数字下变频器的设计与仿真朱建新,沈树群* (北京邮电大学电子工程学院,北京 100876 ) 摘要:本文通过对 ...
微弱信号相关检测程序matlab,基于MATLAB微弱信号的相关检测设计与仿真.pdf
基于MATLAB微弱信号的相关检测设计与仿真.pdf 2010年第1期中国高新技术企业 NO.1.2010 (总第136期) ChinaHi-TechEnterprises (Cumulativet ...
基于matlab的模糊控制器的设计与仿真,基于MATLAB的模糊控制洗衣机的设与仿真详解.doc...
基于MATLAB的模糊控制洗衣机的设计与仿真卫瑶瑶,王胜红 (南京农业大学工学院,210031) 摘要:根据模糊控制的原理对传统洗衣机进行改造,设计了模糊控制系统.通过MATLAB仿真,采用取最大 ...
matlab pid buck,基于MATLAB的BUCK电路设计与PID闭环仿真
文章编号:1009-2552( 2015) 10-0155-04 DOI:10. 13274/j. cnki. hdzj. 2015. 10. 042 基于 MATLAB 的 BUCK 电路设计与 P ...

【SVM】基于matlab的SVM支持向量机训练和测试仿真

1.软件版本

2.核心代码

3.操作步骤与仿真结论

4.参考文献

5.完整源码获得方式

【SVM】基于matlab的SVM支持向量机训练和测试仿真相关推荐

最新文章

热门文章