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Automatic tuning of L 2 ‐SVM parameters employing the extended Kalman filter
Author(s) -
Mu Tingting,
Nandi Asoke K.
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00469.x
Subject(s) - computer science , support vector machine , benchmark (surveying) , kalman filter , nonlinear system , regularization (linguistics) , gradient descent , artificial intelligence , kernel (algebra) , sensitivity (control systems) , extended kalman filter , pattern recognition (psychology) , algorithm , artificial neural network , mathematics , physics , geodesy , quantum mechanics , combinatorics , electronic engineering , engineering , geography
We show that tuning of multiple parameters for a 2‐norm support vector machine (L 2 ‐SVM) could be viewed as an identification problem of a nonlinear dynamic system. Benefiting from the reachable smooth nonlinearity of an L 2 ‐SVM, we propose to employ the extended Kalman filter to tune the kernel and regularization parameters automatically for the L 2 ‐SVM. The proposed method is validated using three public benchmark data sets and compared with the gradient descent approach as well as the genetic algorithm in measures of classification accuracy and computing time. Experimental results demonstrate the effectiveness of the proposed method in higher classification accuracies, faster training speed and less sensitivity to the initial settings.