Study of SVM-Based Incremental Learning for User Adaptation
基于SVM增量学习的用户适应性研究

User adaptation is a critical and important problem. For users' specialization, such as Handwriting, Voice, Drawing Styles, the system is hard to adapt to all users. SVM-based incremental learning can find the most basic feature of different users and cast away the special user's character, so this method can adapt the different users without over fitting. In this paper, the repetitive learning strategy and other two incremental learning algorithms are presented for comparison. Based on theoretical analysis and experimental results, we draw the conclusion that SVM-based incremental learning can solve the user conflict problem.