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