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|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||URI:||http://scholarbank.nus.edu.sg/handle/10635/72830||ISSN:||05379989|
|Appears in Collections:||Staff Publications|
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