Regularized Radial Basis Function Networks: Theory and Applications

Book Info


enlarge picture	*Regularized Radial Basis Function Networks: Theory and Applications*
	Author:	Paul Yee
	ISBN:	0471353493
	Format:	Handover
	Publish Date:	June, 2005




Book Review


*From Book News, Inc.* To serve as a bridge between nonparametric estimation and artificial neural networks, Yee, in the private sector, and Haykin (McMasters U.) examines the interplay of ideas in the two ideas. The specific vehicle for their study is the regularized strict interpolation radial basis function estimate or neural network, one of the better known kernel- based methods for estimation and function approximation. Their target readers are researchers, practitioners, and graduate students in engineering and the sciences.Book News, Inc.®, Portland, OR *Review* "To serve as a bridge between nonparametric estimation and artificial neural networks, Yee...and Haykin...examines the interplay of ides in the two ideas." (SciTech Book News Vol. 25, No. 2 June 2001) "...the book serves as a bridge between the two areas of nonparametric kernel estimations and artificial neural networks...well written..." (Technometrics, Vol. 44, No. 3, August 2002) *Review* "To serve as a bridge between nonparametric estimation and artificial neural networks, Yee...and Haykin...examines the interplay of ides in the two ideas." (SciTech Book News Vol. 25, No. 2 June 2001) "...the book serves as a bridge between the two areas of nonparametric kernel estimations and artificial neural networks...well written..." (Technometrics, Vol. 44, No. 3, August 2002) *Book Description* Simon Haykin is a well-known author of books on neural networks. * An authoritative book dealing with cutting edge technology. * This book has no competition. Regularized Radial Basis Function Networks: Theory and Applications FROM THE PUBLISHER Artificial Neural Networks are an important area of research and there are many practical applications. The Radial Basis Function Network is one of two classes of feedforward networks with applications in artificial neural networks. These applications are in such engineering problems as nonlinear process estimation and control. The present book deals with the design of RBFNs for particular tasks. FROM THE CRITICS SciTech Book News To serve as a bridge between nonparametric estimation and artificial neural networks, Yee...and Haykin...examines the interplay of ides in the two ideas. SciTech Book News To serve as a bridge between nonparametric estimation and artificial neural networks, Yee...and Haykin...examines the interplay of ides in the two ideas. Booknews To serve as a bridge between nonparametric estimation and artificial neural networks, Yee, in the private sector, and Haykin (McMasters U.) examines the interplay of ideas in the two ideas. The specific vehicle for their study is the regularized strict interpolation radial basis function estimate or neural network, one of the better known kernel- based methods for estimation and function approximation. Their target readers are researchers, practitioners, and graduate students in engineering and the sciences. Annotation c. Book News, Inc., Portland, OR (booknews.com)