Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning

Authors:
Hiroshi Dozono;Shigeomi Hara;Shinsuke Itou;Masanori Nakakuni
Affiliations:
Faculty of Science and Engineering, Saga University, Japan 840-8502;Faculty of Science and Engineering, Saga University, Japan 840-8502;Faculty of Science and Engineering, Saga University, Japan 840-8502;Information Technology Center, Fukuoka University, Fukuoka, Japan 814-0180
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

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Abstract

We have proposed Supervised Pareto Learning Self Organizing Maps(SP-SOM) based on the concept of Pareto optimality for the integration of multiple vectors and applied SP-SOM to the biometric authentication system which uses multiple behavior characteristics as feature vectors. In this paper, we examine performance of SP-SOM for the generic classification problem using iris data set. Furthermore, we propose the incremental learning algorithm for SP-SOM and examine effectiveness in a classification problem and adaptation ability to the change of the behavior biometric features by time.