Abstract
Aside from the Expectation-Maximization (EM) algorithm, Least-Mean-Square (LMS) is devised to further train the model parameters as a complementary training algorithm for Cluster-Weighted Modeling (CWM). Due to different objective functions of EM and LMS, the training result of LMS can be used to reinitialize CWM’s model parameters which provides an approach to mitigate local minimum problems.
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Lin, IC., Liou, CY. (2007). Least-Mean-Square Training of Cluster-Weighted Modeling. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_31
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DOI: https://doi.org/10.1007/978-3-540-74695-9_31
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