Author:
Ryotaro Kamimura
Affiliation:
Tokai University, Japan
Keyword(s):
Individually treated neurons, Collectively treated nerons, Information-theoretic learning, Free enrgy, SOM.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Self-Organization and Emergence
;
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 to interact individually treated neurons with collectively treated neurons. The interaction is determined by the interaction parameter a. As the parameter a is increased, the effect of collectiveness is larger. On the other hand, when the parameter a is smaller, the effect of individuality becomes dominant. We applied this method to the self-organizing maps in which much attention has been paid to the collectiveness of neurons. This biased attention has, in our view, shown difficulty in interpreting final SOM knowledge. We conducted an preliminary experiment in which the Ionosphere data from the machine learning database was analyzed. Experimental results confirmed that improved performance could be obtained by controlling the interaction of individuality with collectiveness. In particular, the trustworthiness and continuity are gradually increased by making the parameter a larger. In addition, the class boundaries
become sharper by using the interaction.
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