For an RBF-SVM, two parameters, c and \\(\\gamma \\) , control the SVM performance. In this paper, we present a SVM parameter learning algorithm, DL&BA, effective for learning from big data. The DL&BA algorithm has two stages. At the first stage, we use a distributed learning (...
I thought to use the Random parameter in tune.control() https://rdrr.io/cran/e1071/man/tune.control.html I tried with these two examples below, #TUNE CONTROL WITH RANDOM, trial 1 tune.ctrl2 <- tune.control(random = 1) svm_model2 <- tune(svm , Petal.Width ~ ....
funcNewParameter()*Parameter{ return&Parameter{SvmType:C_SVC,KernelType:RBF,Degree:3,Gamma:0,Coef0:0,Nu:0.5,C:1,Eps:1e-3,P:0.1, NrWeight:0,Probability:false,CacheSize:100,QuietMode:false,NumCPU:-1} } Copy lines Copy permalink
Parameter choice is an open problem in support vector machine (SVM) learning. Whether the parameter takes the form of a scaling vector, a scaling number, or the kernel itself, the fact remains that in the context of non-linear SVMs there are uncountably many solutions. Unfortunately, the ...
the penalty parameter C and the kernel parameterγ.The optimization parameters(C,γ) will make the SVM have the best performance.Firstly,the E=l(sv)n method is proposed to be used to assess the performance of SVM instead of using exhaust algorithm.This algorithm is of high speed and high ...
(SVM)depends on the selection of parameters.SVM parameter selection problems are studied and analyzed,and a parameter optimization method of SVM based on uniform design is proposed.Our method is compared with the parameter optimization methods of SVM based on grid search,particle swarm optimization,...
svmtrain(Ytrain, Xtrain, '-c C_vector(iter)') for different iterations and C_vector=1:10:100 But this does not seem to work and printsError: C <= 0 ps: I have tested thesvmtrain(Ytrain, Xtrain, '-c 1')andsvmtrain(Ytrain, Xtrain, '-c 11'), which are the first two valu...
基于lssvm的威布尔分布形状参数估计 shape parameter evaluation of weibull distribution based on least squares support vector machine EvaluationofWeibullDistribution Parameter Shape BasedonLeast VectorMachine SquaresSupport Zou Ruohe Xinyao,Yao Electronand China (InstituteMieroelectronies,School Technology, of ...
Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to...
A LS-SVM modeling approach for nonlinear distributed parameter processes The distributed parameter system modeling from the input and output data is investigated. The spatio-temporal output of the system is measured at a finite ... C Qi,HX Li - 《IEEE》 被引量: 26发表: 2008年 Dual Extreme ...