提出了一种改进的基于KKT 条件的增量学习算法。

Presents an improved incremental learning algorithm based on KKT conditions.

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基于拉推策略的基本思想，该文提出了文本分类的增量学习模型ICCDP。

Based on DragPush strategy, the paper introduces a text classification incremental learning model, named ICCDP.

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该算法综合了决策树方法和贝叶斯方法的优点，既有良好的可解释性，又有良好的增量学习能力。

The new algorithm combines the merit of decision tree induction method and naive Bayesian method. It retains the good interpretability of decision tree and has good incremental learning ability.

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