Sequential support vector machines
Nando de Freitas, Marta Milo, Peter M. Clarkson, Mahesan Niranjan, Alan H. Gee · 2003
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.