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基于粗糙集的特征选择matlab源代码
- 基于粗糙集的特征选择matlab源代码
relief特征选择matlab程序
- relief 特征选择matlab代码
LDA
- 线性判别分析（LDA）用于特征选择，可以对数据集或者图像提取有用特征，用于分类或者聚类等机器学习应用中-Linear Discriminant Analysis (LDA) for feature selection, application in dataset or image feature extraction, for classification or clustering applications in machine learning
mr-runk
- 基于互信息理论的最大相关排序算法，可应用于各领域的特征选择。-Maximum mutual information based relevance ranking algorithm theory can be applied to all areas of feature selection.
featureselection
- 特征选择方法,matlab实现增 l减 r法 顺序后退法 SFFS 特征选择 顺序前进法特征选择-feature selection
tezhengxuanzhe
- 利用最小互信息实现向量的特征选择，优化分类器的设计，原创-The use of mutual information to achieve the smallest feature selection vectors, optimizing the classifier design, originality
CAGA_O_F
- 关于链式智能体遗传算法用于数值优化和特征选择的论文，可以与我联系相互交流-On the chain-agent genetic algorithm for numerical optimization and feature selection of the papers, you can contact me exchange
pattern-recognition-simulation
- 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类，Bayesian分类，Parzen窗，KNN]和特征选择和降维方法[PCA,LDA]进行了模拟，并给出了各类分类方法的结果，-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results.
Patternrecognition
- 模式识别基本方法matlab源代码，包括最小二乘法、SVM、神经网络、1_k近邻法、剪辑法、特征选择和特征变换。-Basic method of pattern recognition matlab source code, including the least squares method, SVM, neural network, 1_k neighbor method, editing method, feature selection and feature transformatio
ica_appD_demo
- 高校的ICA计算代码，广泛用于特征选择，降维，目标识别等-Colleges and universities ICA calculation code, widely used in feature selection, dimensionality reduction, target identification, etc.
somepapers
- 文献资料，关于遗传算法以及特征选择方面飞一些资料，下载-PAPER
psofeatureselection
- 用粒子群优化算法进行特征选择和SVM参数优化-Using Particle Swarm Optimization algorithm of feature selection and SVM parameter optimization
safeatureselectionpaper
- 模拟退火算法进行特征选择和参数优化的经典论文-Simulated annealing algorithm for feature selection and parameter optimization of the classic papers
LSFS
- 有监督的特征选择和优化程序MATLAB代码，基于最小二乘算法。内有测试数据，和详细程序说明-Least-Squares Feature Selection (LSFS) is a feature selection method for supervised regression and classification. LSFS orders input features according to their dependence on output values. Dependency bet
mrmr
- 特征选择的最大相关最小冗余算法，采用信息理论作为度量标准。-Feature selection algorithm for minimum redundancy and maximum correlation, the use of information theory as a metric.
xinxishang
- ：将信息论中熵的概念应用到特征选择中，定义了两种信息测度评价特征——误差熵和混叠熵，然后阐述了两种定义的不 用物理意义，分析了计算熵中最关键的区间划分问题，并提出一种较好的区间划分方法。-: The concept of entropy in information theory applied to feature selection, the definition of information measure evaluation of two features- error entro
pca
- 该代码为PCA主成分分析，可用于特征选择，选取贡献最大的前三个主成分-The code for the PCA principal component analysis, can be used for feature selection, select the largest contribution to the first three principal components
mds
- 本代码是关于Multi-Dimensional Scaling（MDS）的代码，可以用于特征提取、特征选择，或是矩阵降维。-This file is part of the Matlab Toolbox for Dimensionality Reduction v0.4b. You are free to use, change, or However,
feature_selection
- 两个源文件进行最小冗余和最大相关特征选择，主要用于图像特征选择里面-Two source files and the maximum correlation minimum redundancy feature selection, which is mainly used for image feature selection
基于粒子群优化算法的特征选择SVM分类
- 针对“BreastCancer”数据集，作为对比，第一次对特征集直接进行SVM分类，第二次使用粒子群算法进行特征选择后再进行SVM分类。并且对比和分析了两次分类的结果。(For "BreastCancer" data set, as a comparison, the first time the feature set is directly classified by SVM, and the second time the feature set is selected