Analysis of switching dynamics with competing support vector machines
Resource
IEEE Transactions on Neural Networks 15 (3): 720-727
Journal
IEEE Transactions on Neural Networks
Journal Volume
15
Journal Issue
3
Pages
720-727
Date Issued
2004
Author(s)
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.
Type
journal article
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