资源列表
xiangqiAL
- 象棋经典算法文档， 包括所有经典程序实现原理，和原代码 很经典 象棋经典算法文档， 包括所有经典程序实现原理，和原代码 很经典-Chess classical algorithm documentation, including all the classic principles of program implementation, and the original code is the classic chess classical algorithm document
Flood2
- 神经网络开源C++源程序,基于visual Studio.net 2008环境开发-neural neatworks open source code(C++)，developed in visual Studio.net 2008
fuzzylite-5.0-win32
- fuzzylite is a free and open-source fuzzy logic control library programmed in C++ for multiple platforms (Windows, Linux, Mac, iOS). Its goal is to allow you to easily create fuzzy logic controllers in a few steps utilizing object-oriented programmin
Softmax_exercise
- Softmax用于多分类问题，本例是将MNIST手写数字数据库中的数据0-9十个数字进行分类，其中训练样本有6万个，测试样本有1万个数字是0~9-Softmax for multi classification problems, the present case is the handwritten data MNIST digital 0-9, classification, training samples which have 60,000, there are 10,000 test
ExerciseSelf-Taught-Learning
- Soft-taught leaning是用的无监督学习来学习到特征提取的参数，然后用有监督学习来训练分类器.-Soft-taught leaning unsupervised learning is to learn the parameters of feature extraction, followed by supervised learning to train the classifier.
MNIST数据集
- 利用pycharm对mnist数据哭进行直接解压缩操作，得到所有的图片和标签(Using pycharm to wept MNIST data directly, get all the pictures and labels)
Sparse-Autoencoder
- 稀疏编码算法是一种无监督学习方法，它用来寻找一组“超完备”基向量来更高效地表示样本数据-2006 A fast learning algorithm for deep belief nets
LabelMeToolbox
- LabelMe is a WEB-based image annotation tool that allows researchers to label images and share the annotations with the rest of the community
moead
- 包括近年来Q.F.ZHANG 关于基于分解的多目标优化算法的一系列的文章资料。-Including the recent QFZHANG decomposition based on multi-objective optimization of a series of articles data.
53607900Autoencoder_Code
- 用SAE 深度学习方法做的高光谱图像分析的一个例子。里面有sae方法还有高光谱图像的特征提取过程。(One example of the depth of learning to do with SAE hyperspectral image analysis. There are sae method has hyperspectral image feature extraction process)
ex5
- 机器学习 5、k - means聚类 开发环境Octave-Programming Exercise 5: K-means Clustering and Principal Component Analysis Machine Learning