DNA分类，包含三中模式识别经典算法的实现：K紧邻，BP神经网络，概率神经网络。...

这是一个笔者撰写改进了别人的隐马尔可夫模型算法的程序。-This is a writer writing improvement of the people"s hidden Markov model algorithm procedures.

遗传算法-Genetic algorithms

遗传算法的四个源程序和源代码，在国内没有过的，看看国外的程序员是怎么编写的-Four genetic algorithm source code and source code, never at home and abroad to see how the programmer is prepared

一个非常不错的BP算法程序，这是一个名为bp.m的文件，对于理解BP算法会很有帮助。-The BP algorithm is a very good program, it is a named bp.m file would be helpful for understanding the BP algorithm.

MATLAb语言的遗传算法工具箱，比较全。-MATLAb language of genetic algorithm toolbox, the entire comparison.

基于遗传算法的人工生命模拟，C++源代码-Based on genetic algorithm artificial life simulation, C++ Source code

吉林大学《计算智能》课程的课件-Jilin University, "Computational Intelligence" Curriculum courseware

经典算法clique的java实现-Classical algorithm clique of the java implementation! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

使用Visual C++ 2003编写的遗传算法库，具有详细的说明文档，使用接口函数传入相关适应度函数与基因个数、迭代次数相关信息，进行目标优化，使用类模板的形式支持函数并行化-Using Visual C++ 2003 prepared by the genetic algorithm library, a detailed description of the document, use the interface function is passed the fitness function associated with the gene number, number of iterations relevant information to carry out objective optimization, the use of class template in the form of support function parallel

提出了基于最小化估计的泛化误差界非优化估计方法。目前大多数的核参数选择方法都是通过极小化了来得到最优参数值，但是求解优化问题的计算代价相当的大，并且不能很好地体现数据的分布特征。本文采用非优化技术，通过极小化泛化误差来优化核及相关参数，由于直接计算最小半径和最大间隔，避免了对优化问题的直接求解，因此可以很好地降低计算代价。并且该方法直接从样本出发，可以很好地体现数据的分布特征，不管数据分布是否均匀都可以适用。给出了基于凸包估计的SVM核选择的模型及实现算法。 -Proposed based on minimizing the estimated generalization error bound of the non-optimal estimation method. Most of the nuclear parameter selection methods are to get the best by minimizing the parameter values, but the computational cost for solving optimization problems is quite large, and can not properly reflect the distribution characteristics of the data. In this paper, the non-optimization technology, by minimizing the generalization error to optimize the nuclear and related parameters, due to the direct calculation of the minimum radius and maximum interval, avoiding the direct solution of the optimization problem, it can very well reduce the computation cost. And that the method directly from the sample one can well reflect the distribution chara