Rough Set Approach to Domain Knowledge Approximation
نویسندگان
چکیده
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.
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ورودعنوان ژورنال:
- Fundam. Inform.
دوره 59 شماره
صفحات -
تاریخ انتشار 2003