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Constructing Sparse Kernel Machines Using Attractors

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84040

Lee,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C51D-1
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.