Author:
Ryotaro Kamimura
Affiliation:
IT Education Center, Japan
Keyword(s):
Self-evaluation, Outer-evaluation, Contradiction Resolution, Information-theoretic Learning, Free Energy, SOM.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
In this paper, we propose a new type of information-theoretic method called ”contradiction resolution.” In this method, we suppose that a neuron should be evaluated for itself (self-evaluation) and by all the other neurons (outer-evaluation). If some difference or contradiction between two types of evaluation can be found, the contradiction should be decreased as much as possible. We applied the method to the self-organizing maps with an output layer, which is a kind of combination of the self-organizing maps with the RBF networks. When the method was applied to the dollar-yen exchange rates, prediction and visualization performance could be improved simultaneously.