Monitoring the Formation of Kernel-Based Topographic Maps in a Hybrid SOM-kMER Model

Authors:
Chee Siong Teh;Chee Peng Lim
Affiliations:
Fac. of Cognitive Sci. & Human Dev., Univ. Malaysia Sarawak, Kota Samarahan;-
Venue:
IEEE Transactions on Neural Networks
Year:
2006

Citing 0
Cited 2

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

Neural Processing Letters
Multi-objective image segmentation with an interactive evolutionary computation approach

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new lattice disentangling monitoring algorithm for a hybrid self-organizing map-kernel-based maximum entropy learning rule (SOM-kMER) model is proposed. It aims to overcome topological defects owing to a rapid decrease of the neighborhood range over the finite running time in topographic map formation. The empirical results demonstrate that the proposed approach is able to accelerate the formation of a topographic map and, at the same time, to simplify the monitoring procedure