Description: 针对SVM法线特征筛选算法仅考虑法线对特征筛选的贡献,而忽略了特征分布对特征筛选的贡献的不足,在对SVM法线算法进行分析的基础上,基于特征在正、负例中出现概率的不同提出了加权SVM法线算法,该算法考虑到了法线和特征的分布.通过试验可以看出,在使用较小的特征空间时,与SVM法线算法和信息增益算法相比,加权SVM法线算法具有更好的特征筛选性能.-Normal feature selection for SVM algorithm only considered normal for the contribution of feature selection, to the neglect of the characteristics of the distribution of feature selection have contributed to the lack of normal SVM algorithm based on the analysis, based on the characteristics of the positive and negative cases emergence of a different probability-weighted normal SVM algorithm, which takes into account the distribution and characteristics of normal. through the test can be seen in the use of smaller feature space, the normal and the SVM algorithm and information gain algorithm, normal weighted SVM algorithm has better performance of feature selection. Platform: |
Size: 4096 |
Author:苏苏 |
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Description: 基于改进SVM的特征选择，可以帮助大家认识SVM-To improve the SVM-based feature selection can help you understand the SVM Platform: |
Size: 134144 |
Author:cail1986 |
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Description: 用遗传算法进行特征选取和svm参数优化的程序。遗传算法工具箱goat已在压缩包 需要安装libsvm就可以直接运行。数据集采用UCI中的german数据集，并完成归一化操作-Genetic algorithm with feature selection and parameter optimization svm procedures. Genetic Algorithm Toolbox in goat need to install libsvm package can be run directly. UCI data sets used in the german data set, and complete normalization operation Platform: |
Size: 139264 |
Author:覃茂运 |
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Description: A distributed PSOSVM hybrid system with feature selection and parameter optimization
-Abstract
This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to
improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the
continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM
data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a
heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the
client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also
achieve high classification accuracy.
# 2007 Elsevier B.V. All rights reserved. Platform: |
Size: 565248 |
Author:alice |
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Description: SVM 特征选取的c代码，训练正负两类样本快速提取-C code for SVM feature selection, training, rapid extraction of the two kinds of positive and negative Platform: |
Size: 1024 |
Author:xu |
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Description: The title paper is: "Wrapper Feature Selection Optimized SVM Model for Demand Forecasting" Platform: |
Size: 222208 |
Author:faisal |
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Description: Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernel
parameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature selection is another factor that impacts
classifi cation accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM
classifi cation accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. Platform: |
Size: 141312 |
Author:payal |
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Description: 基于自适应性特征选择和SVM图片分类研究-Based on the adaptive feature selection and the SVM classification images Platform: |
Size: 1465344 |
Author:张炜结 |
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Description: 在生物信息学中，SVM-RFE是一个强大的特征选择算法。这是一个不错的选择以避免过度拟合特性高的数量。-SVM-RFE is a powerful feature selection algorithm in bioinformatics. It is a good choice to avoid overfitting when the number of features is high. Platform: |
Size: 6144 |
Author:李星星 |
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Search - svm Feature selection