Incremental training for SVM-based classification with keyword adjusting |
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Authors: | Sun?Jin-wen Yang?Jian-wu Lu?Bin Email author" target="_blank">Xiao?Jian-guoEmail author |
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Institution: | (1) National Key Laboratory for Text Processing, Institute of Computer Science and Technology, Peking University, 100871 Beijing, China |
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Abstract: | This paper analyzed the theory of incremental learning of SVM (support vector machine) and pointed out it is a shortage that
the support vector optimization is only considered in present research of SVM incremental learning. According to the significance
of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the
difference between incremental learning and batch learning through the keyword adjusting. The experimental results show that
the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method.
Foundation item: Supported by the National Information Industry Development Foundation of China
Biography: SUN Jin-wen (1972-), male, Post-Doctoral, research direction: artificial intelligence, data mining and system integration. |
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Keywords: | SVM (support vector machine) incremental training classification keyword adjusting |
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