On-Line One-Class Support Vector Machines. An Application to Signal Segmentation
2003
Conference Paper
ei
In this paper, 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.
Author(s): | Gretton, A. and Desobry, . |
Book Title: | IEEE ICASSP Vol. 2 |
Journal: | IEEE ICASSP Vol. 2 |
Pages: | 709-712 |
Year: | 2003 |
Month: | April |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | IEEE ICASSP |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PostScript
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BibTex @inproceedings{2134, title = {On-Line One-Class Support Vector Machines. An Application to Signal Segmentation}, author = {Gretton, A. and Desobry, .}, journal = {IEEE ICASSP Vol. 2}, booktitle = {IEEE ICASSP Vol. 2}, pages = {709-712}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = apr, year = {2003}, doi = {}, month_numeric = {4} } |