@INPROCEEDINGS { Rsds3807,
author = {Yokomori, Takashi and Kobayashi, Satoshi}, 
title = {Inductive Learning of Regular Sets From Examples: A Rough Set Approach}, 
booktitle = {Soft Computing}, 
conference = {International Workshop on Rough Sets and Soft Computing}, 
pages = {69--72}, 
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 discusses a learning algorithm for regular sets in the model of learning in the limit from complete data (i.e., positive and negative data). It is shown that any regular set is R-definable for some kinds of equivalence relations R of finite index. We then show that regular sets allow knowledge base system formulations, and present table representations for regular sets. This enables us to deal with regular sets in the framework of rough sets using the table formulation, and present an algorithm for learning regular sets in the limit from complete data.}, 
keywords = {computational learning theory, regular sets, learning in the limit, rough set theory (RST), }, 
}