Abstract
A Cellular Neural Networks (CNN)-based fast pattern classification algorithm utilizing the most likely path finding feature of the dynamic programming is proposed. Previous study shows that the dynamic programming for the most likely path finding algorithm can be implemented with CNN. If exemplars and test patterns are assigned as the goals and the start positions, respectively, on the CNN-based dynamic programming, the paths from test patterns to their closest exemplars are found with the optimality feature of the CNN-based dynamic programming. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding. Simulation results are included.
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© 2003 Springer-Verlag Berlin Heidelberg
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Kim, H., Oh, T., Na, S., Yoon, C. (2003). Pattern Classification with Parallel Processing of the Cellular Neural Networks-Based Dynamic Programming. In: Zhou, X., Xu, M., Jähnichen, S., Cao, J. (eds) Advanced Parallel Processing Technologies. APPT 2003. Lecture Notes in Computer Science, vol 2834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39425-9_33
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DOI: https://doi.org/10.1007/978-3-540-39425-9_33
Publisher Name: Springer, Berlin, Heidelberg
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