Title :
Nonlinear mapping with minimal supervised learning
Author :
Tolat, Viral V. ; Peterson, Allen M.
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., CA, USA
Abstract :
The problem of interpolating unknown mappings from known mappings is addressed. This problem arises when a large number of mappings must be learned and it is impractical to train the network on all possible mappings. Described is a network model that can learn nonlinear mappings with a minimal amount of supervised training. A combination of supervised and supervised learning is used to train the network. It is shown that the network is able to interpolate mappings on which it has not been previously trained
Keywords :
interpolation; learning systems; neural nets; interpolate; known mappings; minimal supervised learning; network model; nonlinear mappings; supervised learning; unknown mappings; Associative memory; Computer networks; Control systems; Neural networks; Process control; Supervised learning; Unsupervised learning;
Conference_Titel :
System Sciences, 1990., Proceedings of the Twenty-Third Annual Hawaii International Conference on
Conference_Location :
Kailua-Kona, HI
DOI :
10.1109/HICSS.1990.205113
