Abstract
In this paper, we propose a new methodology for decomposing pattern classification problems based on the class relations among training data. We also propose two combination principles for integrating individual modules to solve the original problem. By using the decomposition methodology, we can divide a K-class classification problem into \(\left( {\begin{array}{*{20}c}K \\2 \\\end{array} } \right)\) relatively smaller two-class classification problems. If the twoclass problems are still hard to be learned, we can further break down them into a set of smaller and simpler two-class problems. Each of the two-class problem can be learned by a modular network independently. After learning, we can easily integrate all of the modules according to the combination principles to get the solution of the original problem. Consequently, a K-class classification problem can be solved effortlessly by learning a set of smaller and simpler two-class classification problems in parallel.
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© 1997 Springer-Verlag Berlin Heidelberg
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Lu, BL., Ito, M. (1997). Task decomposition based on class relations: A modular neural network architecture for pattern classification. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032491
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DOI: https://doi.org/10.1007/BFb0032491
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