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INTELLIGENCE COMPUTATIONAL

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COMPUTATIONAL INTELLIGENCE

Implementation of a Deep MLP Classifier using Autoencoders and Self-Organizing Maps

for Feature Extraction

Adrian Horzyk

LABORATORY CLASSES

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Use SOM in your Deep MLP Classifier

In the first layer of the already developed MLP network, add the unsupervised trained SOM for initial features extraction and develop a deep MLP Classifier for the Iris or Wine data. Use all output nodes of the SOM as inputs to the MLP network, as well as raw inputs x1, x2, …, xn. Compare results with the already developed solutions.

(3)

Use SOM in your Deep MLP Classifier

First, create a SOM network and train it to get groups of training samples represented by its nodes.

Second, use all SOM outputs computed for each original input data and original raw input data x1, x2, …, xn to stimulate the MLP network instead of using only the original input data.

(4)

Use an Autoencoder

in your Deep MLP Classifier

First, create an autoencoder and train it using the previously implemented backpropagation algorithm.

Next, combine this autoencoder with the already developed (deep) MLP network.

Compare results on Iris, Wine, and other training datasets with the already developed solutions.

(5)

Bibliography and References

Cytaty

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