DocumentCode
2918962
Title
Learning method based on minimization of knowledge representation cost
Author
Khouas, Saliha
Author_Institution
Lab. de Recherche en Inf., Univ. de Paris-Sud, Orsay, France
fYear
1991
fDate
13-15 Aug 1991
Firstpage
377
Lastpage
382
Abstract
The author introduces a practical method based on optimization of numerical criteria to define an interpretation task and a graph clustering task to learn intermediate abstractions as generalizations of similar subgraph groups. The dynamic of the system is organized as an evolutionary process which is based on genetic algorithms. The set of abstractions used in the conceptual graph constitutes the population to evolve in order to optimize the performance of the interpretation task
Keywords
genetic algorithms; graph theory; knowledge representation; learning systems; conceptual graph; evolutionary process; genetic algorithms; graph clustering task; intermediate abstractions; interpretation task; knowledge representation cost; machine learning; minimization; numerical criteria; optimization; Computer vision; Cost function; Data analysis; Genetic algorithms; Knowledge representation; Learning systems; Machine learning; Minimization methods; Optimization methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control, 1991., Proceedings of the 1991 IEEE International Symposium on
Conference_Location
Arlington, VA
ISSN
2158-9860
Print_ISBN
0-7803-0106-4
Type
conf
DOI
10.1109/ISIC.1991.187387
Filename
187387
Link To Document