Cycle Association

用MATLAB编写的支持向量机，实现了线性可分与线性不可分的情况，还有非线性支持向量机，里面部分常用的核函数。-Prepared using MATLAB support vector machine, to achieve a linearly separable and linear indivisible, it is still non-linear support vector machine, which part of the kernel function used.

hide markov model(HMM) for matlab including initiate,train,and test three parts -hide markov model (HMM) for Matlab includi Vi initiate, train, and test three parts

学习魔方编程技巧，仅供参考，有信息请反馈。-learning programming skills, for reference purposes only, with information feedback. Thank you

八数码难题求解程序本程序所采用的启发函数,本人不能证明它能解决任何的八数码难题,如果对于较难的问题会产生很多节点,有可能会使空间不足,所以尽量不要出太难的问题难为它.希望广大的人工智能爱好者能写出更好的启发函数.-eight digital problem solving program of the procedures used by the heuristic function, I can not prove that it can resolve any of the eight digital problem, if for the more difficult problems will have many nodes, it is likely to lack of space, so as not up to the difficult problems it hard. I hope the love AI who better to write a heuristic function better.

一套从入门到精通的Matlab学习教材，附有大量例子，包括神经网络源代码-a proficient from entry to the study and teaching of Matlab, with a large number of examples, including source code neural network

利用窗体实现堆排序。并能逐一显示堆排序的过程-Using the form to achieve Heap Sort

一个坦克大战的源码，适合VC初学者，希望对大家有所帮助！-a Tankedaizhan source code, suitable for beginners VC, and I hope to help you!

关联规则中的频繁项集生成算法Mafia1.4版本，一个效果非常好的算法，可在linux和windows下编译运行。-association rules of frequent item sets Algorithm Mafia1.4 version, a very good effect algorithm, and the Linux compiler running under windows.

一个简单灵活的数据挖掘实验平台，算法基本为空，但提供读数据，基本数据预处理功能，欢迎大家使用，共同提高。-a simple and flexible experimental data mining platform, the basic algorithm for air, but for reading data, the basic data preprocessing functions Everyone is welcome to use together.

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.