RadialBasisNNclassifier: RadialBasisNNclassifier

In Mthrun/Classifiers: Common Supervised Machine Learning Algorithms

RadialBasisNNclassifier

Description

The use of an RBF network is similar to that of an mlp. The idea of radial basis function networks comes from function interpolation theory. The RBF performs a linear combination of n basis functions that are radially symmetric around a center/prototype.

Usage

RadialBasisNNclassifier(TrainData, TrainCls, TestData, PlotIt = FALSE, ...)

Arguments

TrainData	(1:n,1:d) matrix, data Array of n cases withd variables of TrainData or Full data
TrainCls	vector, Array of variable names
TestData	Optional, (1:m,1:d) matrix, data Array of d cases with n variables of TestData
PlotIt	TRUE: plots two plots evaluating the model
...	see rbf

Details

RBF networks are feed-forward networks with one hidden layer. Their activation is not sigmoid (as in MLP), but radially symmetric (often gaussian). Thereby, information is represented locally in the network (in contrast to MLP, where it is globally represented). Advantages of RBF networks in comparison to MLPs are mainly, that the networks are more interpretable, training ought to be easier and faster, and the network only activates in areas of the feature space where it was actually trained, and has therewith the possibility to indicate that it "just doesn't know".

Value

list V with

TestCls	Null if no TestData Given, [1:m] vector of k classes otherwise
Classification	[1:n], classification by RadialBasisNNclassifier of k classes
RBFmodel	Object of rsnns

Author(s)

Michael Thrun

References

Poggio, T. & Girosi, F. (1989), 'A Theory of Networks for Approximation and Learning'(A.I. Memo No.1140, C.B.I.P. Paper No. 31), Technical report, MIT ARTIFICIAL INTELLIGENCE LABORATORY.

Vogt, M. (1992), 'Implementierung und Anwendung von Generalized Radial Basis Functions in einem Simulator neuronaler Netze', Master's thesis, IPVR, University of Stuttgart. (in German)

Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

Zell, A. (1994), Simulation Neuronaler Netze, Addison-Wesley. (in German)

See Also

rbf

Examples

library(FCPS)
data("Chainlink")
Data=Chainlink$Data
Cls=Chainlink$Cls
split=Classifiers::splitquoted(Data,Cls,Percentage = 80)
out=RadialBasisNNclassifier(split$TrainData,TrainCls = split$TrainCls,TestData = split$TestData)
table(out$TestCls,split$TestCls)

Mthrun/Classifiers documentation built on June 28, 2023, 9:28 a.m.