dlvq

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Create and train a dlvq network

Dynamic learning vector quantization (DLVQ) networks are similar to self-organizing maps (SOM, som). But they perform supervised learning and lack a neighborhood relationship between the prototypes.

Usage
dlvq(x, ...)

# S3 method for default dlvq(x, y, initFunc = "DLVQ_Weights", initFuncParams = c(1, -1), learnFunc = "Dynamic_LVQ", learnFuncParams = c(0.03, 0.03, 10), updateFunc = "Dynamic_LVQ", updateFuncParams = c(0), shufflePatterns = TRUE, ...)

Arguments
x

a matrix with training inputs for the network

...

additional function parameters (currently not used)

y

the corresponding target values

initFunc

the initialization function to use

initFuncParams

the parameters for the initialization function

learnFunc

the learning function to use

learnFuncParams

the parameters for the learning function

updateFunc

the update function to use

updateFuncParams

the parameters for the update function

shufflePatterns

should the patterns be shuffled?

Details

The input data has to be normalized in order to use DLVQ.

Learning in DLVQ: For each class, a mean vector (prototype) is calculated and stored in a (newly generated) hidden unit. Then, the net is used to classify every pattern by using the nearest prototype. If a pattern gets misclassified as class y instead of class x, the prototype of class y is moved away from the pattern, and the prototype of class x is moved towards the pattern. This procedure is repeated iteratively until no more changes in classification take place. Then, new prototypes are introduced in the net per class as new hidden units, and initialized by the mean vector of misclassified patterns in that class.

Network architecture: The network only has one hidden layer, containing one unit for each prototype. The prototypes/hidden units are also called codebook vectors. Because SNNS generates the units automatically, and does not need their number to be specified in advance, the procedure is called dynamic LVQ in SNNS.

The default initialization, learning, and update functions are the only ones suitable for this kind of network. The three parameters of the learning function specify two learning rates (for the cases correctly/uncorrectly classified), and the number of cycles the net is trained before mean vectors are calculated.

A detailed description of the theory and the parameters is available, as always, from the SNNS documentation and the other referenced literature.

Value

an rsnns object. The fitted.values member contains the activation patterns for all inputs.

References

Kohonen, T. (1988), Self-organization and associative memory, Vol. 8, Springer-Verlag.

Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of T<U+00FC>bingen. http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

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

Aliases
  • dlvq
  • dlvq.default
Examples
# NOT RUN {
demo(dlvq_ziff)
# }
# NOT RUN {
demo(dlvq_ziffSnnsR)
# }
# NOT RUN {

data(snnsData)
dataset <- snnsData$dlvq_ziff_100.pat

inputs <- dataset[,inputColumns(dataset)]
outputs <- dataset[,outputColumns(dataset)]

model <- dlvq(inputs, outputs)

fitted(model) == outputs
mean(fitted(model) - outputs)
# }
Documentation reproduced from package RSNNS, version 0.4-12, License: LGPL (>= 2) | file LICENSE

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