frbf-package {frbf} | R Documentation |
Package containing the implementation of the Flexible Radial Basis Function.
Package: | remora |
Type: | Package |
Version: | 0.9 |
Date: | 2009-06-13 |
License: | GPL 3.0 |
LazyLoad: | yes |
Depends: | methods |
The package provides the Flexible Radial Basis Function that will create a classification model usable on the predict function.
Fernando Martins Andre Falcao
Maintainers: Fernando Martins <fmp.martins@gmail.com>, Andre Falcao <afalcao@di.fc.ul.pt>
Andre O. Falcao, Thibault Langlois and Andreas Wichert (2006) Flexible kernels for RBF networks. Jornal of Neurocomputing, volume 69, pp 2356-2359. Elsevier.
# infert data is composed by 248 points and will be splitted data(infert) # the training matrix will be use the first 100 points training_matrix <- infert[c(1:100) ,] # the matrix to classify will use all the other points classification_matrix <- infert[c(101:248) ,] # create the model model <- frbf(training_matrix, weighting_function="mahalanobis", class_name = "education", number_clusters = 3, scale_variance = FALSE) # the configuration used on the model print(model@config) # the matrix with the model data information print(model@model) # the function lambda calculated per each cluster print(model@lambda) # the model kernels, the result from the kmeans print(model@kernels) # predict classification <- predict(model, classification_matrix) # the classification points for the last print(classification)