Title :
On-line learning using hierarchical mixtures of experts
Author_Institution :
Nat. Univ. of Singapore, Singapore
Abstract :
In the hierarchical mixtures of experts (HME) framework, outputs from several function approximators specializing in different parts of the input space are combined. Fast learning algorithms derived from the expectation-maximization algorithm have previously been proposed, but they are predominantly for batch learning. In this paper, several online 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
Keywords :
convergence; cooperative systems; hierarchical systems; learning (artificial intelligence); neural net architecture; online operation; piecewise-linear techniques; software performance evaluation; statistics; computational costs; convergence speed; expectation-maximization algorithm; function approximators; hierarchical mixtures of experts; input space specialization; neural network architecture; online learning algorithms; performance; piecewise linear regression task; solution quality; statistical method; storage costs;
Conference_Titel :
Artificial Neural Networks, 1995., Fourth International Conference on
Conference_Location :
Cambridge
Print_ISBN :
0-85296-641-5
DOI :
10.1049/cp:19950580
