贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我...

关于多目标优化的代码，这个代码是一个框架，大家可以修改一下-Multi-objective optimization on the code, the code is a framework, we can make some corrections

介绍了神经 网络中的HOPFIELD算法的C语言实现.-Introduce a neural network algorithm HOPFIELD realize the C language.

SVDD的工具箱，可以很好的处理一类分类问题，详见Support Vector Data Description一文-SVDD tools，can be used to process one class classification problem,for more details ,we refer to "Support Vector Data Description"

vb的波尔兹曼机（含模拟退火算法），有需要的可参考一下 -vb the Boltzmann machine (with simulated annealing algorithm), the need of a reference

遗传算法类文件生成，同系学长的作品，由于一个专业所以课题也十分类似-genetic algorithm types of documents generated, with the seniority system works, as a professional so very similar topic

利用OpenCV库实现的一个Kalman滤波的例子。-A Kalman filter example achieved with OpenCV.

利用双层感知器神经网络处理线性不可分的分类问题-use double perceptron neural networks to handle non-linear classification

基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer t

该程序包包含三个函数，一个用于建立BP神经网络，一个用于训练该网络，最后一个用于测试样本。-The package contains three functions, one for the establishment of BP neural networks, one for training the network, the last one for the test samples.

小波神经网络，两者相互结合，程序简洁易懂！-Wavelet neural network, the two interact and procedures easier to understand!