Title :
A Novel Solution Scheme for the Kernel MSE Model
Author_Institution :
Shenzhen Grad. Sch., Univ. Town of Shenzhen, Shenzhen, China
Abstract :
In this paper we first show that the minimum squared-error solutions of kernel minimum squared-error (KMSE) models are neither unique nor numerically stable. We then propose a novel scheme for KMSE. This solution scheme can produce the unique solution and the maximum between-class margin. This solution has a highly accurate classification rate. The experimental result illustrates the feasibility and effectiveness of MNMSE solution of KMSE.
Keywords :
least mean squares methods; kernel minimum squared-error model; minimum squared-error solution; Artificial intelligence; Cities and towns; Computational complexity; Computational intelligence; Equations; Kernel; Pattern recognition; Transforms; Kernel minimum squared error; Modelling; Pattern Recognition;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.302
