机器学习文本分类的SVM算法实现，VC++ 6.0环境下编译

java的神经网络工具箱-java nerve network toolbox

基因表达式编程 基本算法 能够完成术用于公式发现、函数挖掘，关联规,则挖掘，因子分解，和预测等-gene expression programming algorithm can be used for the completion of the formula found that function Mining, related regulations, mining, factorization, and the forecast

DBSCAN Designed by Dr. Michal aszykowski Department of Chemometrics Institute of Chemistry, The University of Silesia. It s the basic one.

matlab 编写的小波分解程序,进行模式识别-the preparation of the wavelet decomposition matlab procedures, pattern recognition

用遗传算法优化神经网络权值的源程序-genetic algorithm optimization neural network value of the source

本代码用基于steve语言的breve软件编写的，此软件是一种3D人工智能的环境模拟器，可以用来构建AGENT-BASED SYSTEM和模拟机器人行为。本代码实现功能如下：机器人服务模拟。自动取得饮料，递送给客人，客人喝完后，另一种清理机器人处理瓶子并运送垃圾堆。主要为了证明机器人运动中的一种Deliberative设计结构。软件下载地址：http://www.spiderland.org/breve/download.php 打开我上传的文件后，点运行就可以看效果。-The code-based software steve language breve prepared, the artificial intelligence software is a 3D environment simulator can be used to build the AGENT-BASED SYSTEM and robot behavior simulation. Realize this function code is as follows: service robot simulation. Automatically beverages, delivered to the guests, the guests after the finish, another robot cleaning up the bottle and the delivery of garbage treatment. Mainly in order to prove that the movement of a robot design structure Deliberative. Software Download: http://www.spiderland.org/breve/download.php open my uploaded file, you can look at the effect of running.

遗传算法实用程序5（pascal)-GA Utility 5 (pascal)

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

人工智能技术K均值算法，JAVA实现，有需要的下-K means algorithm artificial intelligence technology, JAVA realize there is a need to the next

This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL