Abstract
The probability model is introduced into classification learning in this paper. Kernel covering algorithm (KCA) and maximum likelihood principle of the statistic model combine to form a novel algorithm-the probability model of covering algorithm (PMCA) which not only introduces optimization processing for every covering domain, but offers a new way to solve the parameter problem of kernel function. Covering algorithm (CA) is firstly used to get covering domains and approximate interfaces, and then maximum likelihood principle of finite mixture model is used to fit each distributing. Experiments indicate that optimization is surely achieved, classification rate is improved and the neural cells are cut down greatly through with proper threshold value.
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Zhao, S., Zhang, Yp., Zhang, L., Zhang, P., Zhang, Yc. (2006). Probability Model of Covering Algorithm (PMCA). In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_53
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DOI: https://doi.org/10.1007/11816157_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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