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  Constructing Sparse Kernel Machines Using Attractors

Lee, D., Jung, K.-H., & Lee, J. (2009). Constructing Sparse Kernel Machines Using Attractors. IEEE Transactions on Neural Networks, 20(4), 721-729. doi:10.1109/TNN.2009.2014059.

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Lee, D1, 2, Author              
Jung, K-H, Author
Lee, J, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2009-04
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1109/TNN.2009.2014059
BibTex Citekey: 5776
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Title: IEEE Transactions on Neural Networks
Source Genre: Journal
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Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 20 (4) Sequence Number: - Start / End Page: 721 - 729 Identifier: ISSN: 1045-9227
CoNE: https://pure.mpg.de/cone/journals/resource/954925591430