em算法代码matlab实现-BasicMLAlgs:基础机器学习算法实现，以李航博士的《统计学习方法》为蓝本
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2021-05-25
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BasicMLAlgs-master.zip （82个子文件） 
BasicMLAlgs-master perceptron_matlab 
perceptron_mnist.m 1014B ssign.m 82B perceptron.m 711B ssign1.m 89B rrand.m 100B .gitignore 270B knn_matlab kMin.m 350B knn.m 1KB consData.m 2KB README.md 3KB maxEntropy_matlab epf.m 78B maxEntropy.m 488B pw.m 47B f2.m 78B f1.m 81B logisticRegression_matlab logisticRegression.asv 1KB lossGrd.m 73B ssign.m 80B logisticRegression.m 1KB w.mat 3KB rrand.m 100B naiveBayes_matlab naiveBayes.m 2KB readme.md 0B EM-GMM_matlab EM_GMM1.m 492B trainImage.mat 449B EM_GMM.m 526B EM_GMM2.m 472B GMM.m 1KB gmm_.m 4KB note 
07SVM.docx 2.33MB 09EM笔记.docx 1.7MB 05决策树.docx 67KB 00Summary.docx 1.7MB 03KNN算法.docx 37KB 01统计机器学习方法概论.docx 88KB 08adaboost.docx 16KB 04朴素贝叶斯方法.docx 131KB 10HMM.docx 67KB 06最大熵.docx 1.74MB 02感知器.docx 513KB 11CRF.docx 67KB 06logistic回归.docx 433KB commonFuns_matlab readme.md 0B showImage.m 422B rrand.m 100B consData.m 460B AdaBoost_matlab AdaBoost_8_1.m 677B AdaBoost_8_2.m 664B optimalT.m 251B errortest.m 112B optimalT2.m 251B separateRegion.m 233B data mnist_subset test.m 2KB trainData.mat 341KB trainLabel.mat 201B readme.md 95B testData.mat 332KB testLabel.mat 200B test_subset.m 785B mnist train_binary.csv 73.18MB test.csv 48.72MB train.csv 73.18MB trainData.mat 12MB sample_submission.csv 208KB train-images.idx3-ubyte 44.86MB t10k-images.idx3-ubyte 7.48MB trainLabel.mat 58KB readme.md 323B testData.mat 1.99MB testLabel.mat 10KB t10k-labels.idx1-ubyte 10KB train-labels.idx1-ubyte 59KB decisionTree_matlab constructTree.m 581B
test.txt 1KB test.m 1KB decisionTree.m 3KB computeInfoGain.m 1KB pruningTree.m 1KB featureSelection.m 377B binery2decimal.m 139B decisionTree_exp5_3.m 2KB decisionTree_exp5_2.m 455B资源评论
