Analysis of switching dynamics with competing support vector machines

Author

Chang, Ming-Wei ; Lin, Chih-Jen ; Weng, Ruby C.

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2387

Lastpage

2392

Abstract

We present a framework for the unsupervised segmentation of time series using support vector regression. It is applied to non-stationary time series which alter in time. We follow the architecture of Pawelzik et al. (1996) which consists of competing predictors. In the above paper competing neural networks were used while here we exploit the use of support vector machines, a learning technique. Results indicate that the proposed approach is as good as that of that Pawelzik et al. Differences between the two approaches are also discussed

Keywords

learning (artificial intelligence); learning automata; radial basis function networks; time series; competing support vector machines; learning technique; nonstationary time series; support vector regression; switching dynamics; unsupervised segmentation; Annealing; Binary sequences; Biological neural networks; Computer science; Hidden Markov models; Machine learning; Speech recognition; Statistics; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007515

Filename

1007515