Title :
Sequential support vector machines
Author :
De Freitas, Nando ; Milo, Marta ; Clarkson, Philip ; Niranjan, Mahesan ; gee, anthony
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
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 :
Kalman filters; learning (artificial intelligence); neural nets; optimisation; pattern recognition; signal processing; minimum variance framework; nonstationary signal processing; quadratic optimisation; real-time signal processing; sequential support vector machines; training algorithms; Inference algorithms; Lagrangian functions; Machine vision; Neural networks; Quadratic programming; Signal processing; Signal processing algorithms; Support vector machines; Text categorization; Training data;
Conference_Titel :
Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.
Conference_Location :
Madison, WI
Print_ISBN :
0-7803-5673-X
DOI :
10.1109/NNSP.1999.788120
