Fuzzy clustering is used to cluster data such that each point may pertain to a number of clusters. Fuzzy clustering has applications in the areas like pattern rdoi:10.2139/ssrn.3577549Dahiya, SonikaGosain, AnushikaGupta, SaijalSocial Science Electronic Publishing...
Comparative Performance Of SVM With RBF Kernel Classifier In Predicting Heart Disease For Diagnosed Type 2 Diabetic PatientsClassifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data ...
reproducing kernel particle methodnumerical simulationTo reduce the error on the boundary and improve computational accuracy, the normal derivative of radial basis function (RBF) is introduced into the reproducing kernel particle method (RKPM), and the Hermit-type reproducing kernel particle method (...
at three levels, namely: 1) selecting the model type (e.g., RBF or Kriging), 2) selecting the kernel function type (e.g., cubic or multiquadric kernel in RBF), and 3) determining the optimal values of the typically user-prescribed hyper-parameters (e.g., shape parameter in RBF). ...
at three levels, namely: 1) selecting the model type (e.g., RBF or Kriging), 2) selecting the kernel function type (e.g., cubic or multiquadric kernel in RBF), and 3) determining the optimal values of the typically user-prescribed hyper-parameters (e.g., shape parameter in RBF). ...
Kernel-based multi-layer extreme learning machine (KMLELM) is developed by integrating ML-ELM and a kernel function (RBF). The model is aimed at partially solving the issues of H-ELM and ML-ELM such as eliminating the number of hidden nodes in every layer, achieving optimal model ...
This study compared the classification performance of two classifiers, namely the LS-SVM with RBF kernel, LS-SVM with polynomial kernel and NN method, in 63 classes of IC packaging type dataset in the full model and feature redundant model. The results showed that, for the classification ...