C++ Neural Networks and Fuzzy Logic: Preface

LVQ: Learning Vector Quantizer

You have read about LVQ (Learning Vector Quantizer) in previous chapters. In light of the Kohonen map, it

should be pointed out that the LVQ is simply a supervised version of the Kohonen network. Inputs and

expected output categories are presented to the network for training. You get data clustered, just as a Kohonen

network, according to the similarity to other data inputs.

Counterpropagation Network

A neural network topology, called a counterpropagation network, is a combination of a Kohonen layer with a

Grossberg layer. This network was developed by Robert Hecht−Nielsen and is useful for prototyping of

systems, with a fairly rapid training time compared to backpropagation. The Kohonen layer provides for

categorization, while the Grossberg layer allows for Hebbian conditioned learning. Counterpropagation has

been used successfully in data compression applications for images. Compression ratios of 10:1 to 100:1 have

been obtained, using a lossy compression scheme that codes the image with a technique called vector

quantization, where the image is broken up into representative subimage vectors. The statistics of these

vectors is such that you find that a large part of the image can be adequately represented by a subset of all the

vectors. The vectors with the highest frequency of occurrence are coded with the shortest bit strings, hence

you achieve data compression.