| 摘要 |
PROBLEM TO BE SOLVED: To provide a learning method of an SVM for increasing a learning speed while maintaining the precision of the SVM. SOLUTION: A plural number of training vectors are randomly selected from a total of unused training vectors, and from among the selected training vectors, a vector having the largest error amount is extracted (S120, 125). Subsequently, the extracted vector is added to the already used training vector so as to update the training vector, and the updated training vector is used to learn the SVM (S135, 140). When the largest error amount becomes smaller than a setting valueε(S130) or when the already used training vector becomes larger than a value m (S115), learning of a first phase is stopped. In learning of a second phase, the learning is performed on a predetermined number of or all of the training vectors having a large error amount. COPYRIGHT: (C)2009,JPO&INPIT
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