Title :
Input partitioning to mixture of experts
Author :
Tang, Bin ; Heywood, Malcolm I. ; Shepherd, Michael
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
fDate :
6/24/1905 12:00:00 AM
Abstract :
Given a supervised learning context, the mixture of experts approach uses several neural networks in parallel to provide a modular solution to the overall problem.. Under the mixtures of experts architecture a method for ´designing´ the number of experts and assigning local ´regions´ of the input space to individual experts is investigated. Classification performance and transparency of the scheme is found to be significantly better than that using a standard mixtures of experts
Keywords :
learning (artificial intelligence); neural nets; pattern classification; pattern clustering; self-organising feature maps; input partitioning; learning algorithm; mixtures of experts; neural networks; pattern classification; potential function clustering; self-organizing feature map; transparency; Computer science; Data preprocessing; Decision trees; Feeds; Jacobian matrices; Machine learning; Neural networks; Piecewise linear techniques; Principal component analysis; Supervised learning;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005474
