更多内容请参考http://cn.mathworks.com/help/stats/index.html?s_cid=doc_ftr。
Naive Bayes（朴素贝叶斯）
Factor = NaiveBayes.fit(train_data, train_label);
Scores = posterior(Factor, test_data);
[Scores,Predict_label] = posterior(Factor, test_data);
Predict_label = predict(Factor, test_data);
Random Forest（随机森林）
Factor = TreeBagger(nTree, train_data, train_label);
[Predict_label,Scores] = predict(Factor, test_data);
%scores是语义向量(概率输出)
KNN（K近邻分类器）
新版本将无法使用knnclassify
predict_label = knnclassify(test_data, train_data,train_label, num_neighbors);
mdl = ClassificationKNN.fit(train_data,train_label,'NumNeighbors',1);
predict_label = predict(mdl, test_data);
SVM（支持向量机）
matlab自带svm
Factor = svmtrain(train_data, train_label);
predict_label = svmclassify(Factor, test_data);
libsvm
Factor = svmtrain(train_label, train_data, '-b 1');
[predicted_label, accuracy, Scores] = svmpredict(test_label, test_data, Factor, '-b 1');
集成学习器（Ensembles for Boosting, Bagging, or Random Subspace）
ens = fitensemble(train_data,train_label,'AdaBoostM1' ,100,'tree','type','classification');
predict_label = predict(ens, test_data);
鉴别分析分类器（discriminant analysis classifier）
obj = ClassificationDiscriminant.fit(train_data, train_label);
[predict_label, Scores] = predict(Factor, test_data);