Title :
The concept learning in the theory of rough sets
Author :
Zhang, Qun-Feng ; Jiang, Yu-ting ; Li, Zhi-qiang
Author_Institution :
Key Lab. of Machine Learning & Comput. Intell., Hebei Univ., Baoding
Abstract :
Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.
Keywords :
approximation theory; decision tables; learning (artificial intelligence); rough set theory; PAC-learnability; concept learning; decision table; knowledge reduction; rough sets theory; Computational intelligence; Computer industry; Computer science; Cybernetics; Educational institutions; Knowledge engineering; Machine learning; Machine learning algorithms; Mathematics; Rough sets; Concept Learning; PAC-Learnability; Rough Set; Sample Complexity;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620427
