Description: 一种基于改进主成分分析的人脸识别方法，利用遗传算法进行特征矢量选择-A principal component analysis based on an improved face recognition methods, the use of genetic algorithm feature vector selection Platform: |
Size: 271360 |
Author:李宛霖 |
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Description: 基于改进的独立分量分析的人脸识别方法，,本文将遗传算法(GeneticAlgorithm,GA)应用到独立分量的选择与优化中,从而在
保证较高识别性能的前提下,获得最优的人脸特征子集-Based on an improved independent component analysis for face recognition method, this article will genetic algorithm (GeneticAlgorithm, GA) is applied to independent component selection and optimization, resulting in a higher recognition performance guarantee under the premise was the best facial feature subset Platform: |
Size: 133120 |
Author:李宛霖 |
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Description: 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 Platform: |
Size: 1073152 |
Author:李明 |
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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: paper about Feature selection and parameter optimization for support vector machines:
A new approach based on genetic algorithm with feature chromosomes Platform: |
Size: 861184 |
Author:mar |
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Description: 摘 要: 本文基于Landsat7ETM+全色图像,以浙江省临安市大峡谷镇为研究区,进行基于遗传算法的遥感图像纹理特征选择的研究-Abstract: This article is based Landsat7ETM+ panchromatic image to the Grand Canyon Town, Lin' an City, Zhejiang Province, for the study area, genetic algorithm-based remote sensing image texture feature selection research Platform: |
Size: 753664 |
Author:罗朝辉 |
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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: This submission contains
(1) Journal Article on Zernike Moments, Genetic Algorithm, Feature Selection and Probabilistic Neural Networks.
(2) MATLAB code to do Feature Selection Using Genetic Algorithm.
NB:
(i) This code is short BUT it works incredibly well since we employed GA Toolbox.
(ii) You can run this code directly on your computer since the dataset herein is available in MATLAB software.
(iii) Please do cite my publication to give credit to me (if you use this code).
The citation is as follows:
BABATUNDE Oluleye, ARMSTRONG Leisa J, LENG Jinsong and DIEPEVEEN Dean (2014). Zernike Moments and Genetic Algorithm: Tutorial and Application. British Journal of Mathematics & Computer Science. 4(15):2217-2236.
Or
BABATUNDE, Oluleye and ARMSTRONG, Leisa and LENG, Jinsong and DIEPEVEEN (2014). A Genetic Algorithm-Based Feature Selection. International Journal of Electronics Communication and Computer Engineering: 5(4) 889 905.-This submission contains
(1) Journal Article on Zernike Moments, Genetic Algorithm, Feature Selection and Probabilistic Neural Networks.
(2) MATLAB code to do Feature Selection Using Genetic Algorithm.
NB:
(i) This code is short BUT it works incredibly well since we employed GA Toolbox.
(ii) You can run this code directly on your computer since the dataset herein is available in MATLAB software.
(iii) Please do cite my publication to give credit to me (if you use this code).
The citation is as follows:
BABATUNDE Oluleye, ARMSTRONG Leisa J, LENG Jinsong and DIEPEVEEN Dean (2014). Zernike Moments and Genetic Algorithm: Tutorial and Application. British Journal of Mathematics & Computer Science. 4(15):2217-2236.
Or
BABATUNDE, Oluleye and ARMSTRONG, Leisa and LENG, Jinsong and DIEPEVEEN (2014). A Genetic Algorithm-Based Feature Selection. International Journal of Electronics Communication and Computer Engineering: 5(4) 889 905. Platform: |
Size: 1434624 |
Author:abdalla |
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Description: 用二进制遗传算法做特征选择，此算法效率高，选择的特征数目少。(The binary genetic algorithm is used for feature selection, which has high efficiency and few features.) Platform: |
Size: 3343360 |
Author:xiaohe1234 |
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Search - feature selection genetic algorithm