A neural piecewise linear classifier for pattern classification

Abstract

A neural piecewise linear classifier, based on the Kohonen learning vector quantization (LVQ2) and the Kohonen self-organizing feature map is proposed. The classifier has two stages and a feedback loop. In the first stage, the Kohonen self-organizing feature map network is used to find the approximate position of the prototype vectors for each class. In the second stage, the Kohonen LVQ2 supervised learning algorithm is used to fine-tune the position of the approximate prototype vectors. The accuracy of the classifier is improved by adding an adaptive feedback scheme. Depending on the intrinsic complexity of the class distribution and overall partitioning of the space, the neural classifier automatically increases the number of neurons, improving the error performance. The classifier was tested on a set of high-dimensional real data obtained from ship images. The performance is compared with a piecewise linear tree classifier and a neural classifier. >