matlab 编写的小波分解程序,进行模式识别

Matlab implementation of ID3 and NaiveBayes classifier. It also includes example dataset as well.

简单的遗传算法，可以参考-simple genetic algorithm, can refer to

BP神经网络自适应步长训练算法，采用最小误差法，梯度下降法，自适应调节权值-BP neural network training anaysis, is realized by using error feed back, gradient descent applied updating of synaptic weights

改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algorithm- Guo Tao done optimization algorithm is very effective, and the algorithm is the first basic idea arbitrary n generated random number and then the number li n m randomly selected number, and this number m of a new synthesis of the new middle with n number fitness function of the worst by comparison, if a good case to replace the worst, um, if it even than the best, then ruled the best have replaced. If worse than the worst, a de novo synthesis of new. Followed by the cycle continues. The magic of procedures is GA_crossover () function, the new really good to see heroes who can be improved upon, it generated more than a few with a good.

神经网络隐节点合成算法，用于神经网络泛化能力的提高，和大家共享！-Hidden node neural network synthesis algorithm for neural network generalization capability, and the U.S. share!

该算法包含了一些以MATLB工具箱为基础自己开发的一些遗传算法程序。-The algorithm contains a number based on its own toolbox to MATLB developed some of the genetic algorithm program.

matlab小程序，为初学者提供一些常用的例子-There provide some basic matlab programs that will help a biginer a lot.

一种运算效率较高的遗传算法-a higher genetic algorithms

贝叶斯学习算法分类文本。基于朴素贝叶斯分类器的文本分类的通用算法，是目前所知文本分类算法中最有效的一类-Bayesian text classification algorithm. Based on the simple Bayesian classifier of the definitive text classification algorithm, which is known text classification algorithm the most effective one category

贝叶斯（Bayesion）机器学习相互信息。用于求取熵、联合熵等。改进后可用于随机反演-Bayesian (Bayesion) machine learning of mutual information. Used to obtain entropy, joint entropy and so on. Improved can be used for stochastic inversion