matlab 自带分类器（2012b以上）

train_data是训练特征数据, train_label是分类标签。
Predict_label是预测的标签。
MatLab训练数据, 得到语义标签向量 Scores(概率输出)。

1.逻辑回归(多项式MultiNomial logistic Regression)

Factor = mnrfit(train_data, train_label);Scores = mnrval(Factor, test_data);

2.随机森林分类器（Random Forest）

Factor = TreeBagger(nTree, train_data, train_label);[Predict_label,Scores] = predict(Factor, test_data);

3.朴素贝叶斯分类（Naive Bayes）

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);accuracy = length(find(predict_label == test_label))/length(test_label)*100;

4. 支持向量机SVM分类

Factor = svmtrain(train_data, train_label);predict_label = svmclassify(Factor, test_data);

5.K近邻分类器 （KNN）

Factor = ClassificationKNN.fit(train_data, train_label, 'NumNeighbors', num_neighbors);predict_label = predict(Factor, test_data);[predict_label, Scores] = predict(Factor, test_data);

6.集成学习器（Ensembles for Boosting, Bagging, or Random Subspace）

Factor = fitensemble(train_data, train_label, 'AdaBoostM2', 100, 'tree');Factor = fitensemble(train_data, train_label, 'AdaBoostM2', 100, 'tree', 'type', 'classification');Factor = fitensemble(train_data, train_label, 'Subspace', 50, 'KNN');predict_label = predict(Factor, test_data);[predict_label, Scores] = predict(Factor, test_data);

7. 判别分析分类器（discriminant analysis classifier）

Factor = ClassificationDiscriminant.fit(train_data, train_label);Factor = ClassificationDiscriminant.fit(train_data, train_label, 'discrimType', '判别类型:伪线性...');predict_label = predict(Factor, test_data);[predict_label, Scores] = predict(Factor, test_data);

参考：http://blog.csdn.net/xuhaijiao99/article/details/15027093