Description: 这系列课件系统地讲述了模式识别的基本理论和基本方法。内容涵盖了贝叶斯决策、概率密度函数的估计、线性判别函数、邻近法则、特征的选择和提取、非监督学习、神经网络、模糊模式识别等。-This series of courseware on a pattern recognition system to the basic theory and basic methods. Covers the Bayesian decision-making, the estimated probability density function, linear discriminant function, the neighboring rules, feature selection and extraction, non-supervised learning, neural networks, fuzzy pattern recognition and so on. Platform: |
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Author:yrw |
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Description: 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 between inputs and outputs is evaluated based on an estimator of squared-loss mutual information called LSMI Platform: |
Size: 3072 |
Author:zy |
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Description: 这是一篇关于如何运用超图的理论经过办监督学习用在特征选择上的一篇ecml文章-This is an article on how to use the hypergraph theory after office supervised learning with a ecml articles feature selection Platform: |
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Author:guozhouxiao |
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Description: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in
which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in
practice. More recently, neural network techniques and methods imported statistical learning theory have been receiving
increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes,
sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection
of training and test samples, and performance evaluation.-The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in
which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in
practice. More recently, neural network techniques and methods imported statistical learning theory have been receiving
increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes,
sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection
of training and test samples, and performance evaluation. Platform: |
Size: 1364992 |
Author:jincy |
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Description: 机器学习的范畴，包括SVMs (based on libsvm), k-NN, random forests, decision trees。可以对任意的数据操作-Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems. It works over many datatypes, with a preference for numpy arrays.
For unsupervised learning, milk supports k-means clustering and affinity propagation. Platform: |
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Author:王哲 |
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Search - supervised feature selection