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Analysis of switching dynamics with competing support vector machines

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3 Author(s)
Ming-Wei Chang ; Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Chih-Jen Lin ; R. C. -H. Weng

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.

Published in:

IEEE Transactions on Neural Networks  (Volume:15 ,  Issue: 3 )