Please use this identifier to cite or link to this item:
Title: On-line learning using hierarchical mixtures of experts
Authors: Tham, C.K. 
Issue Date: 1995
Citation: Tham, C.K. (1995). On-line learning using hierarchical mixtures of experts. IEE Conference Publication (409) : 347-351. ScholarBank@NUS Repository.
Abstract: In the Hierarchical Mixtures of Experts (HME) framework, outputs from several function approximators specializing in different parts of input space are combined. Fast learning algorithms derived from the Expectation Maximization algorithm have been proposed, but they are predominantly for batch learning. In this paper, several on-line learning algorithms are developed for the HME. Their performance in a piecewise linear regression task are compared according to criteria such as speed of convergence, quality of solutions and storage and computational costs.
Source Title: IEE Conference Publication
ISSN: 05379989
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Apr 26, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.