@INPROCEEDINGS { Rsds2545,
author = {Skowron, Andrzej and Nguyen, Tuan Trung}, 
title = {Rough Set Approach to Domain Knowledge Approximation}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 2639, 
pages = {221--228}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {May}, 
year = 2003, 
editor = {Wang, Guoying and Liu, Quing and Yao, Yiyu and Skowron, Andrzej}, 
issn = {0302-9743}, 
isbn = {3-540-14040-9}, 
abstract = {Classification systems working on large feature spaces, despite extensive learning, often perform poorly on a group of atypical samples. The problem can be dealt with by incorporating domain knowledge about samples being recognized into the learning process. We present a method that allows to perform this task using a rough approximation framework. We show how human expert's domain knowledge expressed in natural language can be approximately translated by a machine learning recognition system. We present in details how the method performs on a system recognizing handwritten digits from a large digit database. Our approach is an extension of ideas developed in the rough mereology theory.}, 
keywords = {rough mereology, concept approximation, domain knowledge approximation, machine learning, handwritten digit recognition, }, 
}