资源列表
SupportVectorMachinesClassifierBasedonModifyingKer
- 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’sid
PatternRecong
- 图像模式识别的各种算法及其编程实现,包括模板匹配分类器、几何分类器和神经网络分类器等，可用于手写数字的识别。-A variety of image pattern recognition algorithm and its programming, including template matching classifier, geometric classifier and neural network classifiers, etc., can be used for handwritte
GA
- 遗传算法源码的具体实现步骤，提供了遗传算法初学者详细的实现方法-the detailed GA classes and realizes,for the first learner a good conductives
matlab16neuronetwork
- 神经网络的相关资料 神经网络的基础知识及其在MATLAB中的实现-matlab16neuro network
SVMcode
- 基于svm的机器学习文本分类方法，具有很好的借鉴意义-Svm-based machine learning text classification methods, with a good reference
TSP_SA
- Using Simulated annealing to solve TSP
Fuzzymodeling
- 模糊数学建摸课件。讲述的是数学建模中模糊数学的概念，给初学者一个清晰地认识。-Fuzzy modeling courseware. About the mathematical concept of fuzzy mathematical modeling to give beginners a clear understanding of.
obornns
- 佐治亚理工学院的人工智能平台。由Java编写。仅供学习人工智能的网友学习。-Georgia Institute of Technology' s artificial intelligence platform. By Java. User to study artificial intelligence to learn only.
Classify_Homework
- 可以用于函数聚类，以及模式识别和故障诊断的程序，简单实用，有运行结果，准确率高-Clustering can be used for functions, as well as pattern recognition and fault diagnosis procedures, simple and practical, with the results, with high accuracy,
gads_tb
- 《Matlab遗传算法和直接搜索工具箱2用户手册》，PDF格式，官方文档，共343页。-Matlab Generic Algorithm and Direct Search Toolbox 2 User s Guide ,published by Mathworks, 343 pages
java_evolutionary_algorithms
- 用Java实现的进化算法包。包括遗传算法、粒子群算法、memetic算法和进化策略算法。-evolutionary-algorithm Evolutionary Algorithm package implemented using Java. The package serves as a foundation class library, supporting the implementation many variants of Evolutionary Algorith
EDA_Tutorial
- 分布估计算法（EDA）讲义，29页PPT 1. 从GA到 EDA 2. 链接学习与模型 3. 连续EDA 4. EDA 与玻耳兹曼分布 5. 资源 -estimation of distribution algorithm（EDA）tutorial 1. from the GA to the EDA 2. Linking learning and models 3. Continuous EDA 4. EDA with Boltzmann distri