支持向量机（support vector machine， SVM）是在统计学习理论的基础上发展起来的新一代学习算法。它在文本分类、手写识别、图像分类、生物信息学等领域中获得较好的应用。相比于容易过度拟合训练样本的人工神经网络，支持向量机对于未见过的测试样本具有更好的推广能力。 支持向量机以结构化风险最小化为原则，即兼顾训练误差（经验风险）与测试误差（期望风险）的最小化。支持向量机的理论基础就不介绍，请自行看书或谷歌。
首先进行训练，并进行模型保存。
void MainWindow::on_Train_clicked() { // training data int labels[4]= {1, -1, -1, -1}; float trainingData[4][2] = {{501, 10}, {255, 10}, {501, 255}, {10, 501}}; Mat trainingDataMat(4, 2, CV_32FC1, trainingData); Mat labelsMat(4, 1, CV_32SC1, labels); // initial SVM cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create(); svm->setType(cv::ml::SVM::Types::C_SVC); svm->setKernel(cv::ml::SVM::KernelTypes::LINEAR); svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 100, 1e-6)); // train operation svm->train(trainingDataMat, cv::ml::SampleTypes::ROW_SAMPLE, labelsMat); //Save svm->save(modelPath_.toStdString()); }加载训练模型，进行预测。
void MainWindow::on_Test_clicked() { if(!QFileInfo(modelPath_).exists()) return; cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::load(modelPath_.toStdString()); // visual representation int width = 512; int height = 521; Mat image = Mat::zeros(height, width, CV_8UC3); //prediction cv::Vec3b green(0, 255, 0); cv::Vec3b blue(255, 0, 0); for(int i = 0; i< image.rows; i++) { for(int j = 0; j < image.cols; j++) { cv::Mat sampleMat = (cv::Mat_<float>(1, 2)<<j, i); float respose = svm->predict(sampleMat); if(respose == 1) image.at<cv::Vec3b>(i,j) = green; else if(respose == -1) image.at<cv::Vec3b>(i,j) = blue; } } int thickness = -1; int lineType = cv::LineTypes::LINE_8; cv::circle(image, cv::Point(501, 10), 5, cv::Scalar(0, 0, 0), thickness, lineType); cv::circle(image, cv::Point(255, 10), 5, cv::Scalar(255, 255, 255), thickness, lineType); cv::circle(image, cv::Point(501, 255), 5, cv::Scalar(255, 255, 255), thickness, lineType); cv::circle(image, cv::Point(10, 501), 5, cv::Scalar(255, 255, 255), thickness, lineType); thickness = 2; lineType = cv::LineTypes::LINE_8; cv::Mat sv = svm->getSupportVectors(); for (int i = 0; i < sv.rows; i++) { const float* v = sv.ptr<float>(i); cv::circle(image, cv::Point((int)v[0], (int)v[1]), 6, cv::Scalar(128, 128, 128), thickness, lineType); } cv::imshow("SVM Simple Example", image); }