frbf-package {frbf}R Documentation

Flexible Radial Basis Function Package

Description

Package containing the implementation of the Flexible Radial Basis Function.

Details

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.

Author(s)

Fernando Martins Andre Falcao

Maintainers: Fernando Martins <fmp.martins@gmail.com>, Andre Falcao <afalcao@di.fc.ul.pt>

References

Andre O. Falcao, Thibault Langlois and Andreas Wichert (2006) Flexible kernels for RBF networks. Jornal of Neurocomputing, volume 69, pp 2356-2359. Elsevier.

See Also

kmeans prcomp

Examples

# 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) 

[Package frbf version 1.0.1 Index]