Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Rough mereological foundations for design, analysis, synthesis, and control in distributed systems
Information Sciences: an International Journal - From rough sets to soft computing
Rough mereology in information systems. A case study: qualitative spatial reasoning
Rough set methods and applications
Constructing rough mereological granules of classifying rules and classifying algorithms
Technologies for constructing intelligent systems
Rough set approach to domain knowledge approximation
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
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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.