Analysis of Switching Dynamics with Competing Support Vector Machine

Abstract

We present a framework for the unsupervised segmentation\r\nof switching dynamics using support vector machines.\r\nFollowing the architecture by Pawelzik et al., where annealed competing\r\nneural networks were used to segment a nonstationary time\r\nseries, in this paper, we exploit the use of support vector machines,\r\na well-known learning technique. First, a new formulation of support\r\nvector regression is proposed. Second, an expectation-maximization\r\nstep is suggested to adaptively adjust the annealing parameter.\r\nResults indicate that the proposed approach is promising.