DocumentCode
343511
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
fYear
1999
fDate
36373
Firstpage
31
Lastpage
40
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/NNSP.1999.788120
Filename
788120
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