利用卷及神经网络加svm进行分类我要分享

Classification using volume and neural network plus SVM

数据特征提取 CNNSVM-master SVM 神经网络分类 CNN-SVM

关注次数: 374

下载次数: 1

文件大小: 17684KB

代码分类: 图像处理

开发平台: matlab

下载需要积分: 1积分

版权声明:如果侵犯了您的权益请与我们联系,我们将在24小时内删除。

代码描述

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.


代码预览

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