Empirische Inferenz
Conference Paper
2003
On-Line One-Class Support Vector Machines. An Application to Signal Segmentation
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 |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | IEEE ICASSP |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{2134,
title = {On-Line One-Class Support Vector Machines. An Application to Signal Segmentation},
journal = {IEEE ICASSP Vol. 2},
booktitle = {IEEE ICASSP Vol. 2},
abstract = {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.},
pages = {709-712},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = apr,
year = {2003},
slug = {2134},
author = {Gretton, A. and Desobry, .},
month_numeric = {4}
}