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.
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Dozono, H., Hara, S., Itou, S., Nakakuni, M. (2009). Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning. In: PrÃncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_7
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DOI: https://doi.org/10.1007/978-3-642-02397-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02396-5
Online ISBN: 978-3-642-02397-2
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