基于BP算法的贝叶斯网络参数学习， 实现了图形话的节点表示和样本生成， 图形话的结果分析...

用于神经网络分析与设计-for Neural Network Analysis and Design

直觉模糊代数，是学习的一篇好文章，值得-intuition fuzzy algebra, the study is a good article, it is worth a look at

遗传算法优化神经网络结构和链接权，采用浮点数编码-Genetic algorithm neural network structure and link the right to use floating-point encoding

java数据挖掘算法，里面有c45,chc,fss,id3,nb等方面的算法，及相关源代码-data mining algorithms, and they C45, CHC, FSS, id3, nb areas of algorithms, and related source code

支持向量机完整版（SVM）可以用来进行设别训练-Full version of support vector machine (SVM) can be used to set up other training

java神经网络工具箱-neural network toolbox

对于含有噪声的的数字图像模式“1、2、3、4”，使用“hopfield”神经网络，进行联想、识别，以去除噪声的影响。-for containing noise of the digital image model "1,2,3,4", "hopfield" neural network, Lenovo, identification, in order to remove noise impact.

Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42. 使用神经网络集成方法诊断糖尿病,肝炎,乳腺癌症的案例研究. -Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.

遗传算法的实现,c++/vc++/遗传算法的学习安例!-genetic algorithm to achieve, c/vc/GA-cases of learning!

首先采用某种编码方式将解空间映射到编码空间，每个编码对应问题的一个解，称为染色体或个体。一般通过随机方法确定起始的一群个体，称为种群，在种群中根据适应值或某种竞争机制选择个体，使用各种遗传操作算子产生下一代如此进化下去，直到满足期望的终止条件。-begin using some form of coding mapping to the solution space coding space, each encoding a corresponding solution to the problem, known as chromosome or individual. General randomly determined by the initial group of individuals, called stocks, the stocks based on fitness or some mechanism to choose the individual competition, the use of genetic Operator evolution of the next generation so on, until the termination meet expectations conditions.