Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees

Jiri Grim, Pavel Pudil

2014

Abstract

We compare two probabilistic approaches to neural networks - the first one based on the mixtures of product components and the second one using the mixtures of dependence-tree distributions. The product mixture models can be efficiently estimated from data by means of EM algorithm and have some practically important properties. However, in some cases the simplicity of product components could appear too restrictive and a natural idea is to use a more complex mixture of dependence-tree distributions. By considering the concept of dependence tree we can explicitly describe the statistical relationships between pairs of variables at the level of individual components and therefore the approximation power of the resulting mixture may essentially increase. Nonetheless, in application to classification of numerals we have found that both models perform comparably and the contribution of the dependence-tree structures decreases in the course of EM iterations. Thus the optimal estimate of the dependence-tree mixture tends to converge to a simple product mixture model. Regardless of computational aspects, the dependence-tree mixtures could help to clarify the role of dendritic branching in the highly selective excitability of neurons.

Paper Citation

in Harvard Style

Grim J. and Pudil P. (2014). Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 65-75. DOI: 10.5220/0005077500650075

in Bibtex Style

@conference{ncta14,
author={Jiri Grim and Pavel Pudil},
title={Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={65-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005077500650075},
isbn={978-989-758-054-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees
SN - 978-989-758-054-3
AU - Grim J.
AU - Pudil P.
PY - 2014
SP - 65
EP - 75
DO - 10.5220/0005077500650075