遗传算法的示例

遗传算法的源程序例子，对于初学者有很大帮助，建议多-Genetic algorithm source code examples are very helpful for beginners, the proposed multi-

用c++ 开发的中国象棋，其中有网络版和单机版 ，但由于水平有下限，单机版做得很差，电脑智商较低，党调整到很难实几分钟 电脑才能走一步。其中参考了Pc gameing这本书，这本书讲得真好。-with the development of the Chinese chess, which is the network version and the single version, but a lower level. has done a poor job of a single computer, computer lower IQ, and the party to be very difficult to adjust a few minutes a computer can go one step further. With reference to the Pc gameing this book, this book put a shot.

神经网络方面源码 神经网络方面源码 神经网络方面源码 -neural network source neur al network source neur al network source aspects source neural network neural network source neur al network source neural network area source

矩阵计算库，经改编过的，可以扩充，可读性很好。-matrix calculation library, adapted too, can be expanded, good readability.

Supervised Image Segmentation Using Markov Random Fields

北大的SVM课件，对于从事模式识别学习和研究的入门者有一定的帮助-SVM ppt from Peking Univ., it may be useful to the learners or researchers who are engaged on pattern identification.

企业竞争力关键决定因素的直觉模糊识别模型，值得借鉴-competitiveness of enterprises key determinants of intuition fuzzy recognition model, worth learning

张文修老师主编的详细介绍遗传算法及其数学基础的一本书，研究遗传算法的一本很好的参考书，特别推荐！-Edited by Prof. Zhang Wenxiu, detailed genetic algorithm and its mathematical foundations, a very good reference book to study the genetic algorithm, strongly recommended!

本程序是具有进化能力的人工生命floy，你会看到越来越具有智能的floy会自发的产生。 -this evolutionary process is the ability of artificial life floy, you will see more intelligent the floy will spontaneously emerge.

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.