tailieunhanh - Báo cáo hóa học: " Research Article Kernel Affine Projection Algorithms"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Kernel Affine Projection Algorithms | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 784292 12 pages doi 2008 784292 Research Article Kernel Affine Projection Algorithms Weifeng Liu and Josts C. Príncipe Department of Electrical and Computer Engineering University of Florida Gainesville FL 32611 USA Correspondence should be addressed to Weifeng Liu weifeng@ Received 27 September 2007 Revised 23 January 2008 Accepted 21 February 2008 Recommended by Anibal Figueiras-Vidal The combination of the famed kernel trick and affine projection algorithms APAs yields powerful nonlinear extensions named collectively here KAPA. This paper is a follow-up study of the recently introduced kernel least-mean-square algorithm KLMS . KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise boosting performance. More interestingly it provides a unifying model for several neural network techniques including kernel least-mean-square algorithms kernel adaline sliding-window kernel recursive-least squares KRLS and regularization networks. Therefore many insights can be gained into the basic relations among them and the tradeoff between computation complexity and performance. Several simulations illustrate its wide applicability. Copyright 2008 W. Liu and J. C. Príncipe. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION The solid mathematical foundation wide and successful applications are making kernel methods very popular. By the famed kernel trick many linear methods have been recast in high dimensional reproducing kernel Hilbert spaces RKHS to yield more powerful nonlinear extensions including support vector machines 1 principal component analysis 2 recursive least squares 3 Hebbian algorithm 4 Adaline 5 and so forth. More recently a kernelized