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.
Relation:
IEEE Transactions on Neural Networks 15(3),720-727
