最近，本人要做个小东西，使用SVM对8类三维数据进行分类，搜索网上，发现大伙讨论的都是二维数据的二分类问题，遂决定自己研究一番。本人首先参考了opencv的tutorial，这也是二维数据的二分类问题。然后通过学习研究，发现别有洞天，遂实现之前的目标。在这里将代码贴出来，这里实现了对三维数据进行三类划分，以供大家相互学习。

#include "stdafx.h"
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>

using namespace cv;
using namespace std;

int main()
{

    //--------------------- 1. Set up training data randomly ---------------------------------------
    Mat trainData(100, 3, CV_32FC1);
    Mat labels   (100, 1, CV_32FC1);

    RNG rng(100); // Random value generation class

    // Generate random points for the class 1
    Mat trainClass = trainData.rowRange(0, 40);
    // The x coordinate of the points is in [0, 0.4)
    Mat c = trainClass.colRange(0, 1);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * 100));
    // The y coordinate of the points is in [0, 0.4)
    c = trainClass.colRange(1, 2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * 100));
	// The z coordinate of the points is in [0, 0.4)
    c = trainClass.colRange(2, 3);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * 100));

    // Generate random points for the class 2
    trainClass = trainData.rowRange(60, 100);
    // The x coordinate of the points is in [0.6, 1]
    c = trainClass.colRange(0, 1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.6*100), Scalar(100));
    // The y coordinate of the points is in [0.6, 1)
    c = trainClass.colRange(1, 2);
    rng.fill(c, RNG::UNIFORM, Scalar(0.6*100), Scalar(100));
	 // The z coordinate of the points is in [0.6, 1]
    c = trainClass.colRange(2, 3);
    rng.fill(c, RNG::UNIFORM, Scalar(0.6*100), Scalar(100));

    

    // Generate random points for the classes 3
    trainClass = trainData.rowRange(  40, 60);
    // The x coordinate of the points is in [0.4, 0.6)
    c = trainClass.colRange(0,1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.4*100), Scalar(0.6*100));
    // The y coordinate of the points is in [0.4, 0.6)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(0.4*100), Scalar(0.6*100));
	// The z coordinate of the points is in [0.4, 0.6)
    c = trainClass.colRange(2,3);
    rng.fill(c, RNG::UNIFORM, Scalar(0.4*100), Scalar(0.6*100));



    //------------------------- Set up the labels for the classes ---------------------------------
    labels.rowRange( 0,  40).setTo(1);  // Class 1
    labels.rowRange(60, 100).setTo(2);  // Class 2
	labels.rowRange(40, 60).setTo(3);  // Class 3


    //------------------------ 2. Set up the support vector machines parameters --------------------
    CvSVMParams params;
    params.svm_type    = SVM::C_SVC;
    params.C           = 0.1;
    params.kernel_type = SVM::LINEAR;
    params.term_crit   = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);

    //------------------------ 3. Train the svm ----------------------------------------------------
    cout << "Starting training process" << endl;
    CvSVM svm;
    svm.train(trainData, labels, Mat(), Mat(), params);
    cout << "Finished training process" << endl;

	 Mat sampleMat = (Mat_<float>(1,3) << 50, 50,10);
     float response = svm.predict(sampleMat);
	 cout<<response<<endl;

	 sampleMat = (Mat_<float>(1,3) << 50, 50,100);
     response = svm.predict(sampleMat);
	 cout<<response<<endl;

	 sampleMat = (Mat_<float>(1,3) << 50, 50,60);
     response = svm.predict(sampleMat);
	 cout<<response<<endl;
	
    waitKey(0);
}

使用SVM对多类多维数据进行分类