遗传算法的源代码，使用c++语言编写，结构清晰，注释完整，明了易懂...

高人用C＋＋写的ALOPEX(即模式提取算法)实现程序，程序相当精炼，值得大家研究！-expert C++ to write the ALOPEX (model extraction algorithm) procedures, procedures considerable refining, we should study!

本书专门介绍遗传算法中的仿生学计算算法的资料，是一本适合于人工智能初学者的好书。-book devoted to the genetic algorithm bionics algorithm information, is a suitable for beginners books on artificial intelligence.

　基于VB的遗传算法软件实现 在程序中,FitnessValue (i) 为适应度值数组、avFit2nessValue (100) 为归一化适应度值数组、Population2 Chrom(i ,j) 为遗传个体的等位基因值、Popsize 为种群中的个体数,CHROMLENGTH为一母体对的等位基因 总数。-VB-based genetic algorithm software implementation in the proceedings, FitnessValue (i) In order to meet an array value, avFit2nessValue (100) for the normalized fitness value of the array, Population2 Chrom (i, j) for the genetic value of individual alleles, Popsize for stocks in the number of individuals, CHROMLENGTH for one parent to the total number of alleles.

BP算法实现异或问题,采用S型函数的前向多层神经网络及其逆推学习算法-BP algorithm differences or problems, use of the S-Function to the multilayer neural networks and learning algorithms Backstepping

Schapire 所著的Boosting算法的教程。 对目前物体识别和机器学习领域中广泛应用的Boosting算法展开了深入浅出的描述，还有很多Toy Examples。-Tutorial for Boosting algorithm by Schapire

this about the artificial neural networks which uses the back propogation method.-this is about the artificial neural networks which uses the back propogation method.

2值图像的扫描线2值聚类算法，可实现2值图像的准确聚类-2 the value of the image scan line 2, the value of clustering algorithm can realize the exact 2-level image clustering

差分进化算法的一篇全国优秀硕士论文，目前国内对此的研究还处于初步阶段-Differential Evolution of a national excellent master

粒子群算法工具箱，有DEMO程序，可以对16中常见函数测试，并附有神经元网络的训练算法-Particle swarm optimization toolbox, there are DEMO program, you can test for 16 common functions, together with the training algorithm for neural networks

以黎曼几何为理论依据，基于S.Amari的修正核函数思想提出了两种新的保角变换，用其对核函数进行数据依赖性改进，进一步提高支持向量机分类器泛化能力。以人工非线性分类问题 为对象进行研究，仿真实验结果表明采用新保角映射可以快速显著地改善分类器泛化性能，而且能大幅度地减少支持向量的数目。-Two novel conformal transformations were proposed based on the Riemannian geometry theory and S.Amari’sidea.And the kernel function was modified by the transformation in a data-dependent way.Our experimental results for theartificial nonlinear data set show that the generalization performance of support vector machines classifier is improvedremarkably and the number of support vectors is decreased greatly.