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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/18226

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/18226

Title:	Analysis of Switching Dynamics with Competing Support Vector Machine
Authors:	張洺偉;林智仁;翁久幸 Chang, Ming-Wei;Lin, Chih-Jen;Weng, Ruby C.
Date:	2004-05
Issue Date:	2008-12-19 14:56:55 (UTC+8)
Abstract:	We present a framework for the unsupervised segmentation of switching dynamics using support vector machines. Following the architecture by Pawelzik et al., where annealed competing neural networks were used to segment a nonstationary time series, in this paper, we exploit the use of support vector machines, a well-known learning technique. First, a new formulation of support vector regression is proposed. Second, an expectation-maximization step is suggested to adaptively adjust the annealing parameter. Results indicate that the proposed approach is promising.
Relation:	IEEE Transactions on Neural Networks 15(3),720-727
Data Type:	article
DOI link:	http://dx.doi.org/10.1109/IJCNN.2002.1007515
DOI:	10.1109/IJCNN.2002.1007515
Appears in Collections:	[Department of Statistics] Periodical Articles

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