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在opencv3.0中，提供了一个ml.cpp的文件，这里面全是机器学习的算法，共提供了这么几种：
1、正态贝叶斯：normal Bayessian classifier 我已在另外一篇博文中介绍过：在opencv3中实现机器学习之：利用正态贝叶斯分类
2、K最近邻：k nearest neighbors classifier
3、支持向量机：support vectors machine 请参考我的另外一篇博客：在opencv3中实现机器学习之：利用svm(支持向量机)分类
4、决策树： decision tree
5、ADA Boost：adaboost
6、梯度提升决策树：gradient boosted trees
7、随机森林：random forest
8、人工神经网络：artificial neural networks
9、EM算法：expectation-maximization
这些算法在任何一本机器学习书本上都可以介绍过，他们大致的分类过程都很相似，主要分为三个环节：
一、收集样本数据sampleData
二、训练分类器mode
三、对测试数据testData进行预测
不同的地方就是在opencv中的参数设定，假设训练数据为trainingDataMat,且已经标注好labelsMat。待测数据为testMat.
1、正态贝叶斯
// 创建贝叶斯分类器 Ptr<NormalBayesClassifier> model=NormalBayesClassifier::create(); // 设置训练数据 Ptr<TrainData> tData =TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); //训练分类器 model->train(tData); //预测数据 float response = model->predict(testMat);
2、K最近邻
Ptr<KNearest> knn = KNearest::create(); //创建knn分类器 knn->setDefaultK(K); //设定k值 knn->setIsClassifier(true); // 设置训练数据 Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); knn->train(tData); float response = knn->predict(testMat);
3、支持向量机
Ptr<SVM> svm = SVM::create(); //创建一个分类器 svm->setType(SVM::C_SVC); //设置svm类型 svm->setKernel(SVM::POLY); //设置核函数; svm->setDegree(0.5); svm->setGamma(1); svm->setCoef0(1); svm->setNu(0.5); svm->setP(0); svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01)); svm->setC(C); Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); svm->train(tData); float response = svm->predict(testMat);
4、决策树： decision tree
Ptr<DTrees> dtree = DTrees::create(); //创建分类器 dtree->setMaxDepth(8); //设置最大深度 dtree->setMinSampleCount(2); dtree->setUseSurrogates(false); dtree->setCVFolds(0); //交叉验证 dtree->setUse1SERule(false); dtree->setTruncatePrunedTree(false); Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); dtree->train(tData); float response = dtree->predict(testMat);
5、ADA Boost：adaboost
Ptr<Boost> boost = Boost::create(); boost->setBoostType(Boost::DISCRETE); boost->setWeakCount(100); boost->setWeightTrimRate(0.95); boost->setMaxDepth(2); boost->setUseSurrogates(false); boost->setPriors(Mat()); Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); boost->train(tData); float response = boost->predict(testMat);
6、梯度提升决策树：gradient boosted trees
此算法在opencv3.0中被注释掉了，原因未知，因此此处提供一个老版本的算法。
GBTrees::Params params( GBTrees::DEVIANCE_LOSS, // loss_function_type 100, // weak_count 0.1f, // shrinkage 1.0f, // subsample_portion 2, // max_depth false // use_surrogates ) ); Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); Ptr<GBTrees> gbtrees = StatModel::train<GBTrees>(tData, params); float response = gbtrees->predict(testMat);
7、随机森林：random forest
Ptr<RTrees> rtrees = RTrees::create(); rtrees->setMaxDepth(4); rtrees->setMinSampleCount(2); rtrees->setRegressionAccuracy(0.f); rtrees->setUseSurrogates(false); rtrees->setMaxCategories(16); rtrees->setPriors(Mat()); rtrees->setCalculateVarImportance(false); rtrees->setActiveVarCount(1); rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 5, 0)); Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); rtrees->train(tData); float response = rtrees->predict(testMat);
8、人工神经网络：artificial neural networks
Ptr<ANN_MLP> ann = ANN_MLP::create(); ann->setLayerSizes(layer_sizes); ann->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1); ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON)); ann->setTrainMethod(ANN_MLP::BACKPROP, 0.001); Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat); ann->train(tData); float response = ann->predict(testMat);
9、EM算法：expectation-maximization
EM算法与前面的稍微有点不同，它需要创建很多个model,将trainingDataMat分成很多个modelSamples，每个modelSamples训练出一个model
训练核心代码为：
int nmodels = (int)labelsMat.size(); vector<Ptr<EM> > em_models(nmodels); Mat modelSamples; for( i = 0; i < nmodels; i++ ) { const int componentCount = 3; modelSamples.release(); for (j = 0; j < labelsMat.rows; j++) { if (labelsMat.at<int>(j,0)== i) modelSamples.push_back(trainingDataMat.row(j)); } // learn models if( !modelSamples.empty() ) { Ptr<EM> em = EM::create(); em->setClustersNumber(componentCount); em->setCovarianceMatrixType(EM::COV_MAT_DIAGONAL); em->trainEM(modelSamples, noArray(), noArray(), noArray()); em_models[i] = em; } }
预测：
Mat logLikelihoods(1, nmodels, CV_64FC1, Scalar(-DBL_MAX)); for( i = 0; i < nmodels; i++ ) { if( !em_models[i].empty() ) logLikelihoods.at<double>(i) = em_models[i]->predict2(testMat, noArray())[0]; }
这么多的机器学习算法，在实际用途中照我的理解其实只需要掌握svm算法就可以了。
ANN算法在opencv中也叫多层感知机，因此在训练的时候，需要分多层。
EM算法需要为每一类创建一个model。
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原文地址：http://www.cnblogs.com/denny402/p/5032232.html
