中文说明：
先利用卷及神经网络提取数据特征，再加svm进行分类。
English Description：
Firstly, volume and neural network are used to extract data features, and then SVM is added to classify.
Classification using volume and neural network plus SVM
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文件大小： 17684KB
中文说明：
先利用卷及神经网络提取数据特征，再加svm进行分类。
English Description：
Firstly, volume and neural network are used to extract data features, and then SVM is added to classify.
CNNSVM-master
.............\.gitattributes
.............\.gitignore
.............\CNNSVM
.............\......\cnn
.............\......\...\cnnapplygrads.m
.............\......\...\cnnbp.m
.............\......\...\cnnff.m
.............\......\...\cnnnumgradcheck.m
.............\......\...\cnnsetup.m
.............\......\...\cnntest.m
.............\......\...\cnntrain.m
.............\......\...\test_example_CNN.m
.............\......\cnn-model
.............\......\.........\epoch10.mat
.............\......\.........\readme.txt
.............\......\CNN.m
.............\......\CNNSVM.m
.............\......\cnn_predict.m
.............\......\data
.............\......\....\mnist_uint8.mat
.............\......\epoch_by_epoch.m
.............\......\feat-code
.............\......\.........\compute_features.m
.............\......\.........\compute_feature_dim.m
.............\......\.........\compute_gradient.m
.............\......\.........\compute_gradient_features.m
.............\......\.........\compute_gradient_features.m~
.............\......\.........\compute_sphog_features.m
.............\......\.........\concat_features.m
.............\......\.........\cumsum2D.m
.............\......\.........\get_sampling_grid.m
.............\......\.........\normalize_response.m
.............\......\generate_cnn_feature.m
.............\......\Readme.md
.............\......\svm
.............\......\...\display_images.m
.............\......\...\libsvmread.c
.............\......\...\libsvmread.mexw64
.............\......\...\libsvmwrite.c
.............\......\...\libsvmwrite.mexw64
.............\......\...\make.m
.............\......\...\Makefile
.............\......\...\normalize_data.m
.............\......\...\htm" target=_blank>README
.............\......\...\read_data.m
.............\......\...\svmpredict.c
.............\......\...\svmpredict.mexw64
.............\......\...\svmtrain.c
.............\......\...\svmtrain.mexw64
.............\......\...\svm_model_matlab.c
.............\......\...\svm_model_matlab.h
.............\......\svmmnistfea.m
.............\......\util
.............\......\....\allcomb.m
.............\......\....\expand.m
.............\......\....\flicker.m
.............\......\....\flipall.m
.............\......\....\fliplrf.m
.............\......\....\flipudf.m
.............\......\....\im2patches.m
.............\......\....\isOctave.m
.............\......\....\makeLMfilters.m
.............\......\....\myOctaveVersion.m
.............\......\....\normalize.m
.............\......\....\patches2im.m
.............\......\....\randcorr.m
.............\......\....\randp.m
.............\......\....\rnd.m
.............\......\....\sigm.m
.............\......\....\sigmrnd.m
.............\......\....\softmax.m
.............\......\....\tanh_opt.m
.............\......\....\visualize.m
.............\......\....\whiten.m
.............\......\....\zscore.m