The simplest genetic algorithm source code, provided to facilitate the exchange.

本程序是用java语言编写的数据挖掘分类算法中的决策树分类方法c4.5程序代码-this procedure is used java language classification of data mining algorithms decision tree classification code Bank

This a SVM light for those who want to learn how Support Vector Machine work-This is a SVM light for those who want to learn how Support Vector Machine work

实现增量SVM算法，通过对因子的选择增量式的来训练SVM，大大加快了SVM算法的训练速度-To achieve incremental SVM algorithm, through the choice of factors Incremental to train SVM, greatly accelerated the speed of SVM training algorithm. .

基于MATLAB完成的神经网络源程序 大家-Based on the MATLAB neural network to complete the U.S. source

bp神经网络技术用于步态识别的vc源程序

人脸定位实例是模式识别中一个比较复杂的技术 希望能帮到大家-Human Face Localization example is a more complicated pattern recognition technology hope to help everyone

遗传算法工具箱以及用遗传算法找最优值，主函数是zuiyou.m-Genetic Algorithm Toolbox and using genetic algorithms to find optimal values, the main function is zuiyou.m

本程序代码是人工智能中九宫搜索的解决 采用Java语言-the code is AI JiuGongTu search solutions using Java language

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

落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research