Constructing Sparse Kernel Machines Using Attractors
2009
Article
ei
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 |
Department(s): | Empirische Inferenz |
Bibtex Type: | Article (article) |
Digital: | 0 |
DOI: | 10.1109/TNN.2009.2014059 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
Web
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BibTex @article{5776, title = {Constructing Sparse Kernel Machines Using Attractors}, author = {Lee, D. and Jung, K-H. and Lee, J.}, journal = {IEEE Transactions on Neural Networks}, volume = {20}, number = {4}, pages = {721-729}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = apr, year = {2009}, doi = {10.1109/TNN.2009.2014059}, month_numeric = {4} } |