资源列表
wi227
- Contains a common array signal processing algorithm, Pisarenko harmonic decomposition algorithm, Matching Pursuit and orthogonal matching pursuit.
artificial-immune-system-applications-in
- svm2 in Java for Weka plateform , it works well and can be installed by the menegement of main interface.
fcn.berkeleyvision.org
- 图像识别、深度学习。 语言用的是python 2.x ，里面有训练好的模型，在文件夹里都有给出地址，可以直接去下载。也有一个很大的图片包，里面有很多图片，可以直接拿来作为素材用。也可以自己给图片制作数据标签，训练自己的模型。(image recognition deep learning The language used is Python 2.x, and there are training models, which are given in the folder, which c
今天
- 一篇很好的elm 论文，适合正在学习的初学者借鉴。(A good elm paper, suitable for beginners learning from.)
svm
- 利用支持向量机进行人脸识别，需要自己下载SVM工具包(Support vector machine for face recognition, you need to download SVM Toolkit)
basicABC
- 基本人工蜂群算法，带中文注释 可以用来求解优化问题，也可在此基础上改进(Basic Artificial Bee Colony Algorithm with Chinese Notes Can be used to solve the optimization problem can also be improved on this basis)
NCIS
- 用来聚类基因表达数据,结合了基因互作网络(clustering the gene expression data)
Basin_Hopping_algrithom
- A new structure searching algrithom(if you want to find some structure, such as protein structure, cluster structure,material structure, it's very usefully for your research!)
GA优化BP权阈值
- 遗传算法优化BP权值，这是关于基本GA算法和改进GA算法的程序，包括循序选择GA，大变异GA，自适应GA等(Genetic algorithm optimization BP weights, which is about the basic GA algorithm and improved GA algorithm procedures, including sequential selection of GA, big variation GA, adaptive GA and so on
CNN_Torch7-master
- 在ubuntu下实现cnn网络，有相关数据集(CNN_Torch7 ========== This code use the code of Supervised Learning tutorial of Torch7. I add the loading of image by using graphicsMagick for Torch7. 1. for the code intepretation: http://code.madbits.com/wiki/doku.php?id
DeepLearningDropout-master
- dropout和深度学习算法的结合使用，有详细的使用说明和数据集(Three types of layers: - C: convolutional layer (matrix map) - MP: max-pooling layer (matrix map) - F: fully connected layer (vector map) - O: output layer Convolutional Layers: - Scale: scale (size of p
GMM
- 高斯混合模型，通过EM算法迭代得出，可用于语音识别，图像识别等各种领域(Gauss mixture model is iteratively obtained by EM algorithm, and can be used in various fields such as speech recognition and image recognition)