Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
The description identification problem
Artificial Intelligence
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Version spaces without boundary sets
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Tractable approximate knowledge fusion using the Horn fragment of serial propositional dynamic logic
International Journal of Approximate Reasoning
Transactions on rough sets VI
Conjugate information systems: learning cognitive concepts in rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
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The concept learning problem is a general framework for learning concept consistent with available data. Version Spaces theory and methods are build in this framework. However, it is not designated to handle noisy (possibly inconsistent) data. In this paper, we use rough set theory to improve this framework. Firstly, we introduce a rough consistency. Secondly, we define an approximative concept learning problem. Thirdly, we present a Rough Version Space theory and related methods to address the approximative concept learning problem. Using a didactic example, we put these methods into use. An overview of possible extension of this work concludes this article.