Title :
The Learning System of Intuitionistic Optimum Based on Hesitancy Set
Author_Institution :
Dept. of Inf., Liaoning Police Acad., Dalian
Abstract :
The paper describes intuitionistic optimum models for attribute selection of optimum and non-optimum and deals the intuitionistic learning system of analyzing sub-optimum with the degree of knowledge understanding and credit degree of intuitionistic feature. Attribute selection of optimum and non-optimum is performed under both supervised and unsupervised learning. The task of non-optimum analysis is done using a knowledge-based system under supervised learning. The methodology for attribute selection involves minimization of hesitancy set evaluation indices, defined in term of hesitancy function, in connectionist framework.
Keywords :
learning systems; minimisation; set theory; unsupervised learning; attribute selection; connectionist framework; credit degree; hesitancy function; hesitancy set evaluation index minimization; intuitionistic learning system; intuitionistic optimum model; knowledge understanding; knowledge-based system; nonoptimum analysis; supervised learning; unsupervised learning; Character recognition; Control systems; Helium; Humans; Information analysis; Knowledge based systems; Learning systems; Minimization methods; Supervised learning; Unsupervised learning;
Conference_Titel :
Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-0-7695-3161-8
Electronic_ISBN :
978-0-7695-3161-8
DOI :
10.1109/ICICIC.2008.558
