@INPROCEEDINGS { Rsds3559,
author = {Ohsuga, Setsuo and Zhong, Ning}, 
title = {Using Generalizations Distribution Tables as a Hypotheses Search Space For Generalization}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {396--403}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {This paper introduces a new approach for concept learning and rule discovery. The central idea is to substitute Generalizations Distribution Tables (GDT) for the "version space" defined by Mitchell as a hypothesis search space for generalization, in which the probabilistic relationships between concepts and instances over discrete domains are represented. Thus, not only the uncertainty of a rule including the prediction of possible instances can be explicitly represented in the strength of the rule, but also noisy data and missing data can be handled effectively, and biases for search control can be selected. An important application area of our approach is knowledge discovery in real-world databases. In this paper, we focus, on the theoretical basis, basic principles and concepts of our methodology.}, 
keywords = {learning and data mining, concept learning and rule discovery, generalization as search, generalizations distributions tables, hybrid model, }, 
}