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蝙蝠BASVM

于 2021-05-08 发布

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下载积分: 1 下载次数: 34

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代码说明：

说明：本算法是基于蝙蝠优化算法BA优化SVM，可以大大提高VMD的分类准确率，提高优化时间。(This algorithm is based on bat optimization algorithm BA to optimize SVM, which can greatly improve the classification accuracy of VMD and improve the optimization time.)

文件列表：

蝙蝠BASVM\Afit.m, 5965 , 2018-06-19
蝙蝠BASVM\bat.m, 2067 , 2014-11-12
蝙蝠BASVM\BatAlgorithm.pdf, 587199 , 2011-04-25
蝙蝠BASVM\bat_algorithm.m, 4551 , 2018-06-19
蝙蝠BASVM\BA_SVM.m, 5898 , 2018-06-19
蝙蝠BASVM\Fun.m, 529 , 2018-06-19
蝙蝠BASVM\initialization.m, 1845 , 2016-09-08
蝙蝠BASVM\ISSL-Isomap.mat, 6694 , 2018-06-07
蝙蝠BASVM\libsvmread.c, 4063 , 2016-12-22
蝙蝠BASVM\libsvmread.mexw64, 13824 , 2017-06-28
蝙蝠BASVM\libsvmwrite.c, 2341 , 2016-12-22
蝙蝠BASVM\libsvmwrite.mexw64, 12800 , 2017-06-28
蝙蝠BASVM\LTSA.mat, 7088 , 2018-06-07
蝙蝠BASVM\make.m, 888 , 2016-12-22
蝙蝠BASVM\Makefile, 1240 , 2016-12-22
蝙蝠BASVM\PSO.m, 3897 , 2014-06-13
蝙蝠BASVM\simplebounds.m, 269 , 2018-06-19
蝙蝠BASVM\SVM.m, 1652 , 2018-06-07
蝙蝠BASVM\svmpredict.c, 9820 , 2016-12-22
蝙蝠BASVM\svmpredict.mexw64, 27648 , 2017-06-28
蝙蝠BASVM\svmtrain.c, 11818 , 2016-12-22
蝙蝠BASVM\svmtrain.mexw64, 69632 , 2017-06-28
蝙蝠BASVM\svm_model_matlab.c, 8205 , 2016-12-22
蝙蝠BASVM\svm_model_matlab.h, 201 , 2016-12-22
蝙蝠BASVM\新型元启发式蝙蝠算法_黎成.pdf, 735864 , 2014-10-28
蝙蝠BASVM\求解大规模优化问题的可全局收敛蝙蝠算法_黄光球.caj, 755784 , 2014-10-28
蝙蝠BASVM\蝙蝠算法.pdf, 1291010 , 2014-10-28
蝙蝠BASVM, 0 , 2020-09-22

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pudn
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2008-06-06 11:32:54下载

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ch03
介绍matlab中字符串、元胞和构架数组的建立、使用和用法(Introduction matlab in the string array of cellular and framework of the establishment, use and usage)

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2007-07-02 12:44:20下载

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Chaos and the dynamical evolution of barred galaxies

2015-02-07 12:01:17下载

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Advances in data collection and storage capabilities during the past decades have led to an information overload in most sciences. Researchers working in domains as diverse as engineering, astronomy, biology, remote sensing, economics, and consumer transactions, face larger and larger observations and simulations on a daily basis. Such datasets, in contrast with smaller, more traditional datasets that have been studied extensively in the past, present new challenges in data analysis. Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data is the number of variables that are measured on each observation.

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