Constructing sparse kernel machines using attractors.
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.
Lee
Daewon
D
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
Jung
Kyu-Hwan
KH
Lee
Jaewook
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D010363:Pattern Recognition, Automated / Q000379:methods*