利用最大匹配法进行汉语句子的分词 最大匹配算法是最常用的分词算法，简单实用正确率可达到80%以上...

CPN算法的matlab实现，CPN人工神经网络-Matlab algorithm of network，The artificial neural network, network

计算电磁学中的遗传算法，是比较好的优化书籍，欢迎下载使用-Computational electromagnetics in the genetic algorithm is a better optimization of books, welcome to download

nnToolKit 神经网络工具包是基于 MATLAB 神经网络工具箱自行开发的一组神经网络算法函数库，其中的函数在MATLAB环境下均可独立运行，并可打包成DLL组件，这些组件作为独立的COM对象，可以直接被Visual Basic、Visual C++ 、C++ Builder或其它支持COM的高级语言所引用。本工具包中包含的算法包括BP算法中的一些改进算法、模糊神经网络、小波神经网络、遗传算法优化神经网络权值改进算法等，根据需要，读者还可以对该工具包进行扩展，即扩充一些新的函数，以实现特殊的算法需求。-nnToolKit neural network tool kit is based on the MATLAB neural network toolbox developed by a group of neural network algorithm library, which functions in the MATLAB environment can be run independently, and packaged into a DLL components, these components as a separate COM object, can be directly by Visual Basic, Visual C++, C++ Builder or other high-level language support COM quoted. The toolkit includes algorithms, including some of the BP algorithm to improve the algorithm, fuzzy neural network, wavelet neural networks, genetic algorithms to optimize neural network weights to improve the algorithm, if necessary, readers can also expand the tool kit, namely the expansion of some new function, the algorithm in order to achieve sp

Fuzzy Rule Base Implamentation

自己写的一个简单的神经网络(BP)算法，用来解决异或（XOR)问题,初学神经网络的人可以下来看！-Wrote it myself a simple neural network (BP) algorithm, to solve the exclusive-OR (XOR) problem, learning neural network can look at the next!

测试无约束优化定理求解最小值,包括测试数据,在VC下可以实现,已经调试成功.-test unconstrained optimization for minimum theorem, including test data, the VC can be achieved, debugging has been successful.

java extreme programming cookbook

这是我自己做的改进的遗传算法，主要是克服遗传算法的早熟问题-This is what I want done to improve the genetic algorithm, which is aimed at overcoming the genetic algorithm early problems

knn分类模式识别可用于文本挖掘和其他的识别分类,vc代码,-Commission pattern recognition can be used for classification of text mining and other identification classification, vc code,

This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox