Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Rough set approach to domain knowledge approximation
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Approximate Reasoning in MAS: Rough Set Approach
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Approximate Reasoning in MAS: Rough Set Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Toward Rough-Granular Computing
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Research on a Novel Data Mining Method Based on the Rough Sets and Neural Network
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Toward perception based computing: a rough-granular perspective
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Rough sets in perception-based computing
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Eliciting domain knowledge in handwritten digit recognition
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A novel hybrid data mining method based on the RS and BP
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Transactions on Rough Sets V
Rough Set Approach to Domain Knowledge Approximation
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
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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.