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