In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.
| Author(s): | Lee, D. and Jung, K-H. and Lee, J. |
| Journal: | IEEE Transactions on Neural Networks |
| Volume: | 20 |
| Number (issue): | 4 |
| Pages: | 721-729 |
| Year: | 2009 |
| Month: | April |
| Day: | 0 |
| Bibtex Type: | Article (article) |
| DOI: | 10.1109/TNN.2009.2014059 |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@article{5776,
title = {Constructing Sparse Kernel Machines Using Attractors},
journal = {IEEE Transactions on Neural Networks},
abstract = {In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.},
volume = {20},
number = {4},
pages = {721-729},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = apr,
year = {2009},
slug = {5776},
author = {Lee, D. and Jung, K-H. and Lee, J.},
month_numeric = {4}
}