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. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Lin, I. C., & Liou, C. Y. (2007). Least-mean-square training of cluster-weighted modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4669 LNCS, pp. 301–310). Springer Verlag. https://doi.org/10.1007/978-3-540-74695-9_31
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