文件名称:libORF-master
- 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：
- 2015-07-06
- 文件大小：
- 338kb
- 下载次数：
- 0次
- 提 供 者：
- zh***
- 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
针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes:
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear Support Vector Machine, Non-Linear Support Vector Machine (with RBF kernel),
Feed-forward Neural Network, Embedding Neural Network, Convolutional Neural Network, Sparse Autoencoders, Denoising Autoencoders,
Contractive Autoencoders, Stacked Sparse Autoencoders, Self-Taught Learner and Restricted Boltzmann Machines are tested with this version.
- Rest of the methods are not tested hence not supplied and the progress is as follows:
+ Deep Belief Nets with Restricted Boltzmann Machines (not tested)
+ Bayes Nets (tested- refactoring)
+ Hidden Markov Models (tested- refactoring)
+ Conditional Random Fields (work in progress)
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear Support Vector Machine, Non-Linear Support Vector Machine (with RBF kernel),
Feed-forward Neural Network, Embedding Neural Network, Convolutional Neural Network, Sparse Autoencoders, Denoising Autoencoders,
Contractive Autoencoders, Stacked Sparse Autoencoders, Self-Taught Learner and Restricted Boltzmann Machines are tested with this version.
- Rest of the methods are not tested hence not supplied and the progress is as follows:
+ Deep Belief Nets with Restricted Boltzmann Machines (not tested)
+ Bayes Nets (tested- refactoring)
+ Hidden Markov Models (tested- refactoring)
+ Conditional Random Fields (work in progress)
(系统自动生成,下载前可以参看下载内容)
下载文件列表
libORF-master
.............\.directory
.............\.gitattributes
.............\.gitignore
.............\@Color
.............\......\Color.m
.............\@ContractiveAutoencoder
.............\.......................\ContractiveAutoencoder.m
.............\.......................\private
.............\.......................\.......\computeNumericalGradient.m
.............\.......................\.......\contractiveAutoencoderCostBGD.m
.............\.......................\.......\contractiveAutoencoderCostSGD.m
.............\.......................\.......\dNonLinearity.m
.............\.......................\.......\nonLinearity.m
.............\@ConvUtils
.............\..........\.svn
.............\..........\....\all-wcprops
.............\..........\....\entries
.............\..........\....\text-base
.............\..........\....\.........\ConvUtils.m.svn-base
.............\..........\ConvUtils.m
.............\..........\private
.............\..........\.......\maxoutFprop.c
.............\..........\.......\maxoutFprop.mexa64
.............\@DenoisingAutoencoder
.............\.....................\DenoisingAutoencoder.m
.............\.....................\private
.............\.....................\.......\computeNumericalGradient.m
.............\.....................\.......\dNonLinearity.m
.............\.....................\.......\denoisingAutoencoderCostBGD.m
.............\.....................\.......\denoisingAutoencoderCostSGD.m
.............\.....................\.......\nonLinearity.m
.............\@Edge
.............\.....\Edge.m
.............\@EmbeddingNeuralNet
.............\...................\EmbeddingNeuralNet.m
.............\...................\private
.............\...................\.......\dNonLinearity.m
.............\...................\.......\embeddingNeuralNetCost.m
.............\...................\.......\feedForwardENN.m
.............\...................\.......\nonLinearity.m
.............\...................\.......\params2stack.m
.............\...................\.......\stack2params.m
.............\@Img
.............\....\Img.m
.............\....\private
.............\....\.......\display_img.m
.............\@LinearRegressor
.............\................\LinearRegressor.m
.............\................\private
.............\................\.......\costFunctionLinRegL2.m
.............\@LinearSVM
.............\..........\.svn
.............\..........\....\all-wcprops
.............\..........\....\entries
.............\..........\....\text-base
.............\..........\....\.........\LinearSVM.m.svn-base
.............\..........\LinearSVM.m
.............\..........\private
.............\..........\.......\.svn
.............\..........\.......\....\all-wcprops
.............\..........\.......\....\entries
.............\..........\.......\....\text-base
.............\..........\.......\....\.........\linearSVMcostL2.m.svn-base
.............\..........\.......\computeNumericalGradient.m
.............\..........\.......\linearSVMcostL2.m
.............\@LogisticRegressor
.............\..................\LogisticRegressor.m
.............\..................\private
.............\..................\.......\costFunction.m
.............\..................\.......\costFunctionLogRegL2.m
.............\@NaiveBayesGM
.............\.............\.svn
.............\.............\....\all-wcprops
.............\.............\....\entries
.............\.............\....\text-base
.............\.............\....\.........\NaiveBayesGM.m.svn-base
.............\.............\NaiveBayesGM.m
.............\@NeuralNet
.............\..........\.svn
.............\..........\....\all-wcprops
.............\..........\....\entries
.............\..........\....\text-base
.............\..........\....\.........\NeuralNet.m.svn-base
.............\..........\NeuralNet.m
.............\..........\private
.............\..........\.......\.svn
.............\..........\.......\....\all-wcprops
.............\..........\.......\....\entries
.............\..........\.......\....\text-base
.............\..........\.......\