Separable recurrent neural networks treated with stochastic velocities

Authors:
A. Castellanos-Moreno
Affiliations:
Departamento de Física, Universided de Sonora, México
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 7
Cited 0

Self-organizing maps

Self-organizing maps
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier

Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
On the Use of Self-Organizing Maps for Clustering and Visualization

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper gives an overview of how visualization techniques can help us to improve an evolutionary algorithm that trains artificial neural networks. Kohonen's self-organizing maps (SOM) are used for multidimensional scaling and projection of high dimensional search spaces. The SOM visualization technique used here makes visualization of the evolution process easy and intuitive.