Abstract
ARTMAP is one of the famous supervised learning systems. Many learning methods for ARTMAP have been proposed since it was devised as a system to solve Stability-Plasticity Dilemma. AL-SLMAP was implemented by slightly modifying FCSR which was the original learning method for fuzzy ARTMAP (FAM). Although AL-SLMAP can solve pattern recognition problems in a noisy environment more effectively than FCSR, AL-SLMAP is less suitable for region classification problems than FCSR. This means that AL-SLMAP has some problems which do not exist in FCSR. In this paper, we propose a learning method for FAM with explicit and implicit weights to overcome the problems.
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Kamio, T., Mori, K., Mitsubori, K., Ahn, CJ., Fujisaka, H., Haeiwa, K. (2008). Fuzzy ARTMAP with Explicit and Implicit Weights. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_32
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DOI: https://doi.org/10.1007/978-3-540-69158-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
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