DocumentCode :
3491071
Title :
On-line one-class support vector machines. An application to signal segmentation
Author :
Gretton, Arthur ; Desobry, Férédric
Author_Institution :
MPI for Biol. Cybern., Tuebingen, Germany
Volume :
2
fYear :
2003
fDate :
6-10 April 2003
Abstract :
We describe an efficient algorithm to sequentially update a density support estimate obtained using one-class support vector machines. The solution provided is an exact solution, which proves to be far more computationally attractive than a batch approach. This deterministic technique is applied to the problem of audio signal segmentation, with simulations demonstrating the computational performance gain on toy data sets, and the accuracy of the segmentation on audio signals.
Keywords :
audio signal processing; learning automata; SVM; audio signal segmentation; computational performance gain; density support estimate updating; deterministic technique; efficient algorithm; exact solution; on-line one-class support vector machines; toy data sets; Biology computing; Computational modeling; Cybernetics; Detectors; Jet engines; Optimization methods; Performance gain; Support vector machine classification; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-7663-3
Type :
conf
DOI :
10.1109/ICASSP.2003.1202465
Filename :
1202465
Link To Document :
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