Learning Vector Quantization networks are generally considered a powerful pattern recognition tool. Their main drawback, however, is the Competitive Learning algorithm they are based upon, that suffers of the so called underutilized or dead unit problem. To solve this problem, algorithms substantially based on a modified distance calculation, such as the Frequency Sensitive Competitive Learning (FSCL), have been proposed, but their attainable performance strongly depends on the selection of an appropriate number of neurons. This choice generally require knowledge about the number of clusters in the feature space. In this paper we propose a new supervised training algorithm for LVQ neural networks, which provide the optimal number of neurons for each class by dynamically adding or removing neurons on the basis of a measure of their performance. The experimental results, performed on different databases of synthetic data, confirmed the effectiveness of our approach.

A Dynamic Approach to Learning Vector Quantization

DE STEFANO, Claudio;D'ELIA, Ciro;
2004-01-01

Abstract

Learning Vector Quantization networks are generally considered a powerful pattern recognition tool. Their main drawback, however, is the Competitive Learning algorithm they are based upon, that suffers of the so called underutilized or dead unit problem. To solve this problem, algorithms substantially based on a modified distance calculation, such as the Frequency Sensitive Competitive Learning (FSCL), have been proposed, but their attainable performance strongly depends on the selection of an appropriate number of neurons. This choice generally require knowledge about the number of clusters in the feature space. In this paper we propose a new supervised training algorithm for LVQ neural networks, which provide the optimal number of neurons for each class by dynamically adding or removing neurons on the basis of a measure of their performance. The experimental results, performed on different databases of synthetic data, confirmed the effectiveness of our approach.
2004
0769521282
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/5527
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
social impact