Feature Extractions and Recognition Methods for Concept Recognition and its Applications.

点击翻译按钮获取中文摘要

1) Classification of Features: The knowledge that is necessary for judgments are not numerical score like a real number but rough categories. A new concept "granularity" based on the rough categories was introduced. We interpreted discrete data as a grouping problem. Therefore an algorithm for discovering semi-optical answer for a grouping problem was proposed.2) Discovering Prototype: The features that is necessary for judgments are not all knowledge and experiments but some abstract data. From this point of view, an algorithm that discover typical prototype in enormous data was proposed. The prototype is not a point but a object has volume. A prototype update algorithm was also proposed to adapt increasing data with progress of time. On the other hand, we defined the similarity of tree structures. We proposed an algorithm that extracts some typical trees in many trees using the similarity.3) Feature Selection depend on Individual situations: A classifier-independent feature selection method was proposed. On the other hand, a feature selection method that extract optimum features in random selected features was also proposed from a point of view that optimum feature sets are depend on category and/or category set.