英和和英

出典:『Wikipedia』 (2010/12/16 09:54 UTC 版)

英語による解説

引用

Kernel adaptive filtering is an adaptive filtering technique for general nonlinear problems. It is a natural generalization of linear adaptive filtering in reproducing kernel Hilbert spaces. Kernel adaptive filters are online kernel methods, closely related to some artificial neural networks such as radial basis function networks and regularization networks. Some distinguishing features include: The learning process is online, the learning process is convex with no local minima, and the learning process requires moderate complexity.