\      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[None EK"Managed type for struct svm_model.4  !"#$%&'()*+,-./01237 45)*+./, -01236('&%$#"!  None<&Extra parameters of SVM implementationASVM Algorithm with parametersBc-SVC algorithmCnu-SVC algorithmDnu-SVR algorithmEeps-SVR algorithmF One class SVMJ"Kernel function for SVM algorithm.K(Linear kernel function, i.e. dot productL.Gaussian radial basis function with parameter OMSigmoid kernel functionN!Inhomogeneous polynomial functionRR& is a wrapper over foreign pointer to SBSVM problem is a list of maps from training vectors to 1.0 or -1.0TdVector type provides a sparse implementation of vector. It uses IntMap as underlying implementation.\.Default extra parameters of SVM implamentationULike V but with extra parametersVThe V function allows training a R starting from a S by specifying an A and a JWLike  crossvalidate but with extra parametersXStratified cross validationYSave model to the fileZLoad model from the file[Predict a value for T by using R <=?@>ABCDEFGHIJKLMNQOPRSTUVWXYZ['TSJKLMNOOPOPQABCDEFGHHGIGH<=>?@RVUXWZY[<=>?@A BCDEFGHHGIGHJ KLMNOOPOPQR]^      !"#$%&'()*+,-./0123456789::;<=>?@A BCDEFG HIJKLMNOPQRHS&HSvm-0.1.0.3.22-9PSfRFbaCi09L5ZX0eY0Za Data.SVM.RawData.SVM CSvmModel CSvmParametersvm_type kernel_typedegreegammacoef0 cache_sizeepsc nr_weight weight_labelweightnup shrinking probability CKernelType unCKernelTypeCSvmType unCSvmType CSvmProblemlyxCSvmNodeindexvaluec_clone_model_support_vectorsc_svm_destroy_modelc_svm_check_parameterc_svm_load_modelc_svm_save_model c_svm_predictc_svm_cross_validation c_svm_traincSvcnuSvconeClass epsilonSvrnuSvrlinearpolyrbfsigmoid precomputed defaultCParam$fStorableCSvmNode$fStorableCSvmProblem$fStorableCSvmParameter$fStorableCSvmType$fShowCSvmType$fStorableCKernelType$fShowCKernelType$fShowCSvmParameter ExtraParam cacheSize AlgorithmCSvcNuSvcNuSvr EpsilonSvr OneClassSvmepsilon KernelTypeLinearRBFSigmoidPolyModelProblemVectortrain'traincrossValidate' crossValidate saveModel loadModelpredict defaultExtra