IEEE on MRI fuzzy segmentation of brain images related article ,2007

Scalable k-means software and test datasets This package (a Unix tar file, gzipped) contains the source code for the software that was used to run the experiments for the article -Scalable k-means software and test datasets This package (a Unix tar file, gzipped) contains the source code for the software that was used to run the experiments for the article

the matlab code of ginisvm - an svm implementation good at probability regression.-the matlab code of ginisvm- an svm implementation good at probability regression.

k-means算法 自定义数据样本和类中心 已经调试实现 并且有图清晰表示-k-means algorithm for custom data types of samples and testing center has been and realize that has a clear plan

国外的一个A*算法演示，带源代码。作为算法参考非常好-abroad an A* algorithm demonstration, with the source code. As a very good reference algorithm

C++实现的支持向量机神经网络代码，欢迎各位使用-C realized SVM neural network code, welcome you use

神经网络中自组织特征映射模型算法的源程序，属于无导师学习！-Neural network model self-organizing feature map algorithm source, belonging to no mentor to learn!

Collection of 30+ AI Neural Network java applets with documentation.

对模式识别中的字符串模板匹配问题进行的研究，设计了对任意字符串进行匹配和字符串相似度，即列文斯顿距离进行计算的算法-right pattern recognition template matching string for the research and design of the arbitrary strings and string matching similarity, Ken Livingstone distance calculation algorithm

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,

使用神经网络的K算法来对数据进行分类，有具体的实验要求和图-K using the neural network algorithm to classify the data, there is a specific test requirements and plans