文件名称:beiyesigen
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2016-12-05
- 文件大小：
- 70kb
- 下载次数：
- 0次
- 提 供 者：
- 刘*
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
各种贝叶斯，k紧邻，KNN算法实例大集合-Various Bayes, k close, a large collection of KNN algorithm example .............
(系统自动生成,下载前可以参看下载内容)
下载文件列表
beiyesigen
..........\Adaboost
..........\........\adaboost.py
..........\........\README.md
..........\........\testAdaboost.py
..........\bikMeans
..........\........\bikMeans.m
..........\........\README.md
..........\........\testSet.txt
..........\Decision-Tree
..........\.............\README.md
..........\.............\TestTree.py
..........\.............\Tree.py
..........\DeepLearning
..........\............\CNN_cifar-10
..........\............\............\cifar.py
..........\............\CNN_mnist
..........\............\.........\cnn.py
..........\............\.........\data.py
..........\............\.........\trainCNN.py
..........\............\UFLDL
..........\............\.....\stl_exercise
..........\............\.....\............\display_network.m
..........\............\.....\............\feedForwardAutoencoder.m
..........\............\.....\............\initializeParameters.m
..........\............\.....\............\loadMNISTImages.m
..........\............\.....\............\loadMNISTLabels.m
..........\............\.....\............\softmaxCost.m
..........\............\.....\............\softmaxPredict.m
..........\............\.....\............\softmaxTrain.m
..........\............\.....\............\sparseAutoencoderCost.m
..........\............\.....\............\stlExercise.m
..........\............\.....\Vectorization_sparseae_exercise
..........\............\.....\...............................\checkNumericalGradient.m
..........\............\.....\...............................\computeNumericalGradient.m
..........\............\.....\...............................\display_network.m
..........\............\.....\...............................\initializeParameters.m
..........\GMM
..........\...\gmm.m
..........\...\gmm.py
..........\...\README.md
..........\...\testGMM.m
..........\...\testSet.txt
..........\kalmanFilter
..........\............\kalmanFiltering.m
..........\............\KF.m
..........\Kmeans
..........\......\distEclud.m
..........\......\kMeans.m
..........\......\README.md
..........\......\testkMeans.m
..........\......\testSet.txt
..........\KNN
..........\...\datingTestSet2.txt
..........\...\handWritingTest.m
..........\...\KNN.m
..........\...\KNN.py
..........\...\KNNdatgingTest.m
..........\...\README.md
..........\Logistic-regression
..........\...................\gradAscent.m
..........\...................\ImproveStocGradAscent.m
..........\...................\README.md
..........\...................\stocGradAscent.m
..........\...................\testSet.txt
..........\MLP
..........\...\dualperceptron.py
..........\...\perceptron.py
..........\...\testSet.txt
..........\PCA
..........\...\PCA.m
..........\...\README.md
..........\...\testPCA.m
..........\...\testSet.txt
..........\README.md