title: Analysis of switching dynamics with competing support vector machines.
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.
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.
authors.forename: Ming-Wei
authors.initials: MW
authors.lastname: Chang
authors.forename: Chih-Jen
authors.initials: CJ
authors.lastname: Lin
authors.forename: Ruby Chiu-Hsing
authors.initials: RC
authors.lastname: Weng
mesh_terms: D001185:Artificial Intelligence; D012044:Regression Analysis