@INPROCEEDINGS { Rsds3794,
author = {Kobayashi, Satoshi and Yokomori, Takashi}, 
title = {An Extended Rough Set Theory Toward Approximate Learning of Formal Languages}, 
booktitle = {Soft Computing}, 
conference = {International Workshop on Rough Sets and Soft Computing}, 
pages = {14--17}, 
publisher = {The Society for Computer Simulation}, 
address = {San Diego, U.S.A.}, 
month = {November}, 
year = 1994, 
editor = {Lin, Tsau Young and Wildberger, A.M.}, 
isbn = {1-56555-077-3}, 
abstract = {This paper introduces upper and lower approximation of concepts with respect to an infinite concept class C and presents necessary and sufficient conditions for C to have such approximate concepts always in C. Then, we compare these notions with those of the rough set theory and show that the former can be regarded as an extension of the latter. Further, we propose a framework of learning upper and lower approximations of any concept with respect to an infinite concept class, and some efficient learning algorithms are presented.}, 
keywords = {computational learning theory, formal languages, approximate learning, rough set theory (RST), }, 
}