Abstract
Boosting is an effecient method to improve the classification performance. Recent theoretical work has shown that the boosting technique can be viewed as a gradient descent search for a good fit in function space. Several authors have applied such viewpoint to solve the density estimation problems. In this paper we generalize such framework to a specific density model – Gaussian Mixture Model (GMM) and propose our boosting GMM algorithm. We will illustrate the applications of our algorithm to cluster ensemble and short-term traffic flow forecasting problems. Experimental results are presented showing the effectiveness of our approach.
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Wang, F., Zhang, C., Lu, N. (2005). Boosting GMM and Its Two Applications. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_2
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DOI: https://doi.org/10.1007/11494683_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
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