matlabsvr代码-svm-parameter-selection:支持向量机参数选择
matlab
svr代码支持向量机已成为解决各个领域分类和回归问题的重要学习技术。
然而，惩罚参数和内核参数的选择不当会显着降低
SVR
的性能。
自然地，没有启发式算法中低效的搜索策略和较长的搜索时间，元启发式已被引入作为与问题无关的技术，以在广泛的问题中获得可接受的最优值。
因此本程序旨在为SVM参数选择提供元启发式的仿真代码。
如何使用
基本的
GS-SVR
和
GA-SVR
演示可以在matlab-implement文件夹中找到。
[bestCVmse,bestc,bestg]
=
SVMcgForRegress(TrainL,Train,-8,8,-8,8,5,0.4,0.4)
cmd
=
['-c
',num2str(bestc),'
-g
',num2str(bestg),'
-s
3
-p
0.01'];
许多元启发式代码可以在
或
中找到。更多基于元启发式的算法可以通过ABC_SVM来ABC_SVM
。
待续。
调查报告
李赛、方华景和刘晓勇。
《基于正余弦算法的支持向量回归参数优化》。
具有应用程序的专家系统（2017
年）。
(SCI，影响因子：3.928，doi
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