A training algorithm for classification of high-dimensional data

We propose an algorithm for training Multi Layer Preceptrons for classification problems, that we named Hidden Layer Learning Vector Quantization (H-LVQ). It consists of applying Learning Vector Quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rurtherford backscattering spectra and on a benchmark problem of image recognition. It may also be used for efficient feature extraction.