NDLI: New advances on Bayesian Ying-Yang learning system with Kullback and non-Kullback separation functionals

Content Provider	IEEE Xplore Digital Library
Author	Lei Xu
Copyright Year	1997
Description	Author affiliation: Dept. of Comput. Sci., Chinese Univ. of Hong Kong, Shatin, Hong Kong (Lei Xu)
Abstract	In this paper, we extend Bayesian-Kullback Ying-Yang (BKYY) learning into a much broader Bayesian Ying-Yang (BYY) learning system via different separation functionals instead of using only Kullback divergence, and elaborate the power of BYY learning as a general learning theory for parameter learning, scale selection, structure evaluation, regularization and sampling design. Improved criteria are proposed for selecting number of densities on finite mixture and Gaussian mixtures, for selecting number of clusters in MSE clustering, for selecting subspace dimension in PCA related methods, for selecting number of expert nets in mixture of experts and its alternative model and for selecting number of basis functions in RBF nets. Three categories of non-Kullback separation functionals namely convex divergence, L/sub p/ divergence and decorrelation index, are suggested for BYY learning as alternatives for those learning models based on Kullback divergence, with some properties discussed. As examples, the EM algorithms for finite mixture, mixture of experts and its alternative model are derived with convex divergence.
Starting Page	1942
Ending Page	1947
File Size	798020
Page Count	6
File Format	PDF
ISBN	0780341228
DOI	10.1109/ICNN.1997.614196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Learning systems Computer science Sampling methods Principal component analysis Decorrelation Information processing Training data Kernel Density functional theory
Content Type	Text
Resource Type	Article

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