资源列表
属性相似度的云分类器
- matlab环境下，基于云模型的分类器，包括基于例子群优化的云分类器，和属性相似度云分类器。-Matlab environment, based on cloud model for the classification, including examples Swarm Optimization Based on the cloud classifier, and attribute similarity cloud classifier.
TSP和野人过河
- 货郎担问题和野人过河问题，解压后有word文档，里面有详细说明-traveling salesman problem and savage river, unpacked a word document, containing details
NNDemo2.0
- 是一个用MATLAB编的一个系统，是关于各个神经我网络模型和支持向量机的软件包-using MATLAB is a part of a system of various neural network model and I support vector machine packages
LIBSVMsrc
- 一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。-a good LIBSVM JAVA source. They should study and improve SVM academics. Reference. From Data Mining Tool Kit Yale.
VC野人八数码程序
- 用VC编制的集成的野人和八数码演示程序。其中野人程序用动态的效果演示，并能设置进度。八数码程序能根据给出的源状态转换成目标状态。-VC establishment of integrated Savage and eight digital presentations. Savage procedures which use dynamic demonstration effect, and can be programmed to progress. Eight digital process
人工神经网络原理及仿真实例
- 该系统使用极其简便，即使 你对各种网络模型不是很深刻的了解，也可以很好的使用该系统。使用时， 你可以自己修改网络的各种参数，交互性较好，而且该系统通过大量的图示 及参数设置，可以让你了解每个应用实例实现的过程及详细步骤。-The system is extremely easy to use, even if you have to various network model is not very profound understanding can be a good use of the
EightFigure
- 自己用JAVA编的人工智能的A*算法的八数码程序-own use JAVA series of the artificial intelligence of the A * algorithm eight digital procedures
遗传算法matlab
- matlab编写的遗传算法，采用二进制编码，有说明文档。-Matlab prepared by the genetic algorithm, using the binary coding, documentation.
C++033103
- 指纹识别程序，大家看看吧，也许有用，不用客气-fingerprint identification procedures, we look at it, perhaps useful, not polite
Apriori(VC程序)
- 本程序是数据挖掘中的关联规则模型中著名的Aprior算法的VC实现程序，可用于知识发现、数据挖掘、人工智能、模式识别等领域（请先解压文件）-The code is the VC implementation of the well-known Aprior algorithm in Association Rule Model of Data Mining field, can be used in Knowledge Discovery, Data Mining, AI, Pattern Re
指纹识别源代码03
- 这一个指纹识别的源代码程序,相当有收藏价值-that a fingerprint identification of the source code procedures and very collectible value
机器学习
- Mitchell的《机器学习〉随书源码，非常不错-Mitchell's "machine learning> With the source book, very good