Analysis of Switching Dynamics with Competing Support Vector Machines
Date Issued
2002-01-23
Date
2002-01-23
Author(s)
DOI
2006092712285907084
Abstract
We present a framework for the unsupervised
segmentation of time series using support
vector regression. It applies to non-stationary time series which alter in time. We follow the architecture by Pawelzik et al. which consists of competing predictors. In competing Neural Networks were used while here we exploit the use of Support Vector Machines, a new learning technique. Results indicate that the proposed approach is as good as that in . Diferences between the two approaches are also discussed.
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
report
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Format
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