art1
, ART aims at solving the stability/plasticity
dilemma. So the advantage of ARTMAP is that it is a supervised learning mechanism
that guarantees stability.
artmap(x, ...)
"artmap"(x, nInputsTrain, nInputsTargets, nUnitsRecLayerTrain, nUnitsRecLayerTargets, maxit = 1, nRowInputsTrain = 1, nRowInputsTargets = 1, nRowUnitsRecLayerTrain = 1, nRowUnitsRecLayerTargets = 1, initFunc = "ARTMAP_Weights", initFuncParams = c(1, 1, 1, 1, 0), learnFunc = "ARTMAP", learnFuncParams = c(0.8, 1, 1, 0, 0), updateFunc = "ARTMAP_Stable", updateFuncParams = c(0.8, 1, 1, 0, 0), shufflePatterns = TRUE, ...)
rsnns
object. The fitted.values
member of the object contains a
list of two-dimensional activation patterns.
art1
. The two ART1 networks are connected by a map field.
The input of the first ART1 network is the training input, the input of the second network are the target values,
the teacher signals. The two networks are often called ARTa and ARTb, we call them here training data network
and target data network.In analogy to the ART1 and ART2 implementations, there are one initialization function, one learning function, and two update functions present that are suitable for ARTMAP. The parameters are basically as in ART1, but for two networks. The learning function and the update functions have 3 parameters, the vigilance parameters of the two ART1 networks and an additional vigilance parameter for inter ART reset control. The initialization function has four parameters, two for every ART1 network.
A detailed description of the theory and the parameters is available from the SNNS documentation and the other referenced literature.
Grossberg, S. (1988), Adaptive pattern classification and universal recoding. I.: parallel development and coding of neural feature detectors, MIT Press, Cambridge, MA, USA, chapter I, pp. 243--258.
Herrmann, K.-U. (1992), 'ART -- Adaptive Resonance Theory -- Architekturen, Implementierung und Anwendung', 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/
Zell, A. (1994), Simulation Neuronaler Netze, Addison-Wesley. (in German)
art1
, art2
## Not run: demo(artmap_letters)
## Not run: demo(artmap_lettersSnnsR)
data(snnsData)
trainData <- snnsData$artmap_train.pat
testData <- snnsData$artmap_test.pat
model <- artmap(trainData, nInputsTrain=70, nInputsTargets=5,
nUnitsRecLayerTrain=50, nUnitsRecLayerTargets=26)
model$fitted.values
predict(model, testData)
Run the code above in your browser using DataLab