apriori算法c语言版,apriori算法c语言版

用的VC6编的可以识别联机数字，联机英文，及联机汉字-the VC6 series of the figures can be identified on-line, on-line English, and Chinese characters on-line

在人工智能中模式识别比较新的分类算法,支持向量机.用于特征分类.-A new classify arithmetic of pattern-recognition in artificial intelligence,use support vecotr machine to classify the characteristic

bell 实验室周期性关联规则挖掘算法。-Cycle Association

关联模式的Apriori的vc实现，对数据挖掘感兴趣的人一定很希望得到吧！-VC implementation of Apriori based on Associate Pattern, is it what exactly you want in data mining?

该源码用来实现基本粒子群的算法，值得参考-The source algorithm is used to achieve the elementary particle group, worth considering

Simon Haykin的 《Neural NetWorks》例子原码，相当经典。相信很有用，特别SVM PCA等-Simon Haykin "Neural NetWorks" examples of the original code, very classic. I believe very useful, especially in such SVM PCA

OPENCV系列的，TOOLBOX工具，应该挺有用的，大家自己看看吧！-OPENCV series, TOOLBOX tool, it should be quite useless, we all see!

这是个解决八码问题的程序，是外国人编的，用了图形处理技术，请大家参考-This is a solution of 8 yards procedures, the series is a foreigner, his use of graphics processing technology, please refer to

BP神经网络解决异或问题.模式识别常见问题解决-BP neural network to solve XOR problem. Pattern Recognition common problem-solving

自组织系统Kohonen网络模型。对于Kohonen神经网络，竞争是这样进行的：对于“赢”的那个神经元c，在其周围Nc的区域内神经元在不同程度上得到兴奋，而在Nc以外的神经元都被抑制。网络的学习过程就是网络的连接权根据训练样本进行自适应、自组织的过程，经过一定次数的训练以后，网络能够把拓扑意义下相似的输入样本映射到相近的输出节点上。网络能够实现从输入到输出的非线性降维映射结构：它是受视网膜皮层的生物功能的启发而提出的。~..~-Kohonen network model. For Kohonen neural network, competition is this : For the "winner" of neurons c, in its switching around the region neurons in varying degrees, to be excited, and the switching outside the neurons were inhibited. Network learning is a process in the network connecting the right under the training samples for adaptive, self-organizing process, after a certain number of training, network topology can sense similar to the mapping of the input samples similar to the output nodes. Network can be achieved from input to output of nonlinear reduced-dimensional mapping structure : it is subject to retinal cortex of the biological function inspired by. ~ ~ ..