资源列表
classification
- 该程序包实现了几个常用的模式识别分类器算法,包括K近邻分类器KNN、线性判别方程LDF分类器、二次判别方程QDF分类器、RDA规则判别分析分类器、MQDF改进二次判别方程分类器、SVM支持向量机分类器。 主程序中还有接口调用举例,压缩包中还有两个测试数据集文件。-The package to achieve a number of commonly used pattern recognition classifier algorithms, including K neighbor class
libsvm
- libsvm是支持向量机一个重要的核函数的一种svm,可以作为一个工具箱使用。-libsvm support vector machine is an important kernel function svm, can be used as a toolbox .
遗传算法源程序
- 遗传算法源程序-Genetic algorithm source code
bp
- 人工智能,神经网络,反馈算法,用于曲线拟合等-Artificial intelligence, neural networks, feedback algorithm for curve fitting, etc.
bp_flower
- bp神经网络对鸢尾属植物分类,用四个特征进行分类,分类正确率100 - iris data classification using bp neural network
zidongji4
- 元胞自动机算法实现,遗传算法实现,开发环境为MATLAB-Cellular Automata algorithm, genetic algorithm, the development environment for the MATLAB
prediction-GRNN
- GRNN的数据预测-基于广义回归神经网络货运量预测-The data prediction GRNN- Generalized regression neural network based on volume forecast
6
- 人携带狐狸,鹅,豆子安全过河问题,应用各种不同算法,SWI-prolog编程,简单游戏-Person carrying a fox, geese, beans safety problem across the river, the application of different algorithms, SWI-prolog programming, easy Games
PCALDA
- PCA+LDA经典人脸识别算法,先用PCA降维,再用LCA降维-PCA+ LDA classical face recognition algorithms, first PCA dimension reduction, reuse LCA dimension reduction
parzen
- 这是一个模式识别中的parzen窗的一个简单仿真分类实例,其中female.txt和male.txt是训练样本,test.txt是测试样本,分类效果非常好,对于模式学习的初学者将会有很大帮助。-This is a pattern recognition in a simple window parzen Category simulation examples, one of female.txt and male.txt training samples, test.txt is the me
motiondetect
- 本程序是基于VC++和opencv开发的视频入侵检测预警程序,在目标进入危险区域,提示警示语-This procedure is based on VC++ and opencv video intrusion detection development of early warning procedures, the goal of entering the hazardous area, prompted warnings
k-means-segamen-method
- 本实验基于K-Means聚类算法思想实现了字符分割,因为车牌规定是7位的,所以K取7。另外本实验对K-Means算法进行了改进,充分考虑了初始点的设置及迭代结束条件。实验结果证明这种改进的K-Means算法实现车牌字符分割是快速、有效的。-In this study, K-Means clustering algorithm based on the ideology of the character segmentation, because the license plate require
