A LVQ-like Network Approach for Classification

Abstract

The Learning Vector Quantization (LVQ) network is a hybrid network, employing both unsupervised and supervised learning to solve classification problems. It is simple, but some issues exist. For example, the number of hidden nodes has to be decided in advance, and the spread of the data is not considered. In this paper, we propose a LVQ-like network approach to remedy these disadvantages. An iterative self-constructing clustering algorithm is used to determine the number of hidden nodes in the hidden layer. Data are described by clusters with appropriate centers and deviations. As a result, the number of hidden nodes is determined automatically. Also, through the incorporation of adaptive deviations, data can be described more appropriately. Experimental results are shown to demonstrate the effectiveness of the proposed approach.