Abstract: Feature decision means (303) decides a set of features appropriate for pattern identification from a plenty of feature candidates generated by feature candidate generation means (302) by using learning patterns stored in learning, pattern storage means (301). The feature decision means (303) successively decides features according to a reference of information maximization under the condition that the decided feature is known while adding an effective noise to the learning pattern and performs information amount calculation approximately and at a high speed while merging the learning patterns into a set of N elements when required. As a result, it is possible to automatically create a feature set appropriate for pattern identification of a high performance without requiring enormous learning. Moreover, by using a transition table (305) containing transitions between sets, it is possible to perform pattern judgment with a high efficiency.

Abstract: Feature decision means (303) decides a set of features appropriate for pattern identification from a plenty of feature candidates generated by feature candidate generation means (302) by using learning patterns stored in learning, pattern storage means (301). The feature decision means (303) successively decides features according to a reference of information maximization under the condition that the decided feature is known while adding an effective noise to the learning pattern and performs information amount calculation approximately and at a high speed while merging the learning patterns into a set of N elements when required. As a result, it is possible to automatically create a feature set appropriate for pattern identification of a high performance without requiring enormous learning. Moreover, by using a transition table (305) containing transitions between sets, it is possible to perform pattern judgment with a high efficiency.