I recently wrote a paper source, mainly for reference signal with the ICA algori...

计算欧几里德距离C语言源代码,次源码经测试可有效运行,源码简单易懂-Calculation of Euclidean distance C-language source code, sub-source has been tested and can be effectively run, easy-to-read source

一个比较好用的中文分词模块,是车东等人开发的,比较有名,java语言实现-a better quality of the Chinese word module, East cars were developed, more famous, java language!

数据挖掘c45分类算法 数据挖掘c45分类算法-C45 data mining data mining classification algorithms C45 classification algorithm

自适应遗传算法matlab代码.自适应遗传算法的问题是很容易早熟,好处就是速度快 .不过我将在其中会引入一个早熟判定标志的，并设定一个阈值，如此以来，就可以防止早熟现象了 -Adaptive genetic algorithm Matlab code. Adaptive genetic algorithm is very easy precocious, advantage is faster. However, in which I will be introducing an early sign of determination, and set a threshold, so, you can prevent precocious phenomenon

一个采用VC编写的神经网络源代码，供大家参考。-VC prepared using a neural network source code for reference.

face recognition test source code

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.

这是一个的遗传算法程序,开发平台是vc7.0-This is a genetic algorithm process, the development platform is vc7.0

Kohonen的SOFM(自组织特征映射)：这种算法部分收到生物特征影响，在网络输出层内按几何中心或特征进行聚类。-Kohonen the SOFM (self-organizing feature map) : This algorithm received some biological features, the output layer to the network within the geometric center or cluster features.

Kohonen网络的学习过程可描述为：对于每一个网络的输入，只调整一部分权值，使权向量更接近或更偏离输入矢量，这一调整过程就是竞争学习。随着不断的学习过程，所有输入矢量都在输入矢量空间相互分离，形成了各自代表输入空间的一类模式，这就是Kohonen网络的特征自动识别的聚类功能。请解压缩后按照readme提示进行操作。-Kohonen network learning process can be described as follows : for each one network input, only part of adjusting the weights, weight vector closer to or further from the input vector, the adjustment process is competitive learning. With the continuous process of learning, all the input vector in the importation of vector space separate form their own representatives importation of a type of space, and this is a Kohonen network of automatic clustering functions. Please decompress after readme according to instructions.