DocumentCode
288545
Title
A knowledge-based approach to supervised incremental learning
Author
Fu, LiMin ; Hsu, Hui-Hunag ; Principe, Jose C.
Author_Institution
Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA
Volume
3
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
1793
Abstract
How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a rule-based connectionist approach in which old knowledge is preserved by bounding weight modifications. In addition, some heuristics are developed for avoiding overtraining of the network and adding new hidden units. The feasibility of this approach is demonstrated for classification problems including the iris and the promoter domains
Keywords
knowledge based systems; learning (artificial intelligence); neural nets; pattern classification; bounding weight modifications; classification; heuristics; incremental-learning neural network; knowledge-based system; rule-based connectionist; supervised incremental learning; Computer networks; Encoding; Iris; Learning systems; Multidimensional systems; Neural networks; Problem-solving; Real time systems; Uncertainty; Weight measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374428
Filename
374428
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