此代码是用c语言编写的决策树的c4.5代码，它是数据挖掘分类算法中的一种，可以对给定数据集进行分类，挖掘出规则...

使用遗传算法、模拟退火算法求解设计结构矩阵，并进行对比分析-The use of genetic algorithms, simulated annealing algorithm design structure matrix, and a comparative analysis of

【机器学习】文本分类源代码 【机器学习】文本分类源代码 -] [Machine learning text classification source code] [machine learning text classification source code

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.

这是一篇关于文本分类问题的综述，详细的介绍了文本分类的技术发展。-text category

粒子群寻优算法源程序，采用VC+=编写， 比较好用。-Particle

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!

JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.-JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.

神经网络C程序的第9章-neural network C procedures for the Chapter 9

用于程序优化与遗传算法-procedures for optimization and genetic algorithms

软件无线电中信号调制方式的识别――用人工神经网络的方法-Software Defined Radio Signal Modulation Recognition- Using Artificial Neural Networks approach