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自带的机器学习库
knn分类器knn = fitcknn(meas,species,'NumNeighbors',5); CVMdl = crossval(knn); kloss = kfoldLoss(CVMdl); predict(knn,ones(1,size(meas,2))) pca降维:主成分分析//latent:特征值(从大到小),score特征向量 [coeff, score, latent, tsquared, explained] = pca(data); //score即为从大到小排序后的特征矩阵,取前k列即为取样本最具代表性的k个属性 //explained即为每一列对应的影响力,所有列加起来为100 bp神经网络
svm分类器svm = fitcsvm(meas,species); CVMdl = crossval(svm); kloss = kfoldLoss(CVMdl); 朴素贝叶斯naivebayes = fitcnb(meas, species); nb = crossval(naivebayes); kloss = kfoldLoss(nb); 决策树cart分类器cart = fitctree(meas,species); CVMdl = crossval(cart); kloss = kfoldLoss(CVMdl); 随机森林分类器b = TreeBagger(nTree,meas,species,'OOBPrediction','on'); rf = oobError(b); kloss = rf(nTree,1); 集成学习方法ada = fitensemble(meas,species,'AdaBoostM1',100,'Tree','Holdout',0.5); kloss = kfoldLoss(ada,'mode','cumulative'); kloss = kloss(100,1); matlab机器学习库有监督学习无监督学习集成学习 |
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