Sequential support vector machines
- 20 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We derive an algorithm to train support vector machines sequentially. The algorithm makes use of the Kalman filter and is optimal in a minimum variance framework. It extends the support vector machine paradigm to applications involving real-time and non-stationary signal processing. It also provides a computationally efficient alternative to the problem of quadratic optimisation.Keywords
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