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| Adaptation is a critical problem in the design of user-centered human-computer interaction systems. In this paper, an SVM-based incremental learning algorithm is presented to solve this problem for sketch recognition, the goal of which is to achieve adaptive sketch recognition. Our algorithm utilizes only the support vectors instead of all the historical samples, and selects some important samples from all newly added samples as training data. The importance of a sample is measured according to its distance to the hyper-plane of the SVM classifier. Theoretical analysis, experimentation, and evaluation of our algorithm in our on-line graphics recognition system are presented to show the effectiveness of this algorithm. According to our experiments, this algorithm can reduce both the training time and the required storage space for the training dataset to a large extent with very little loss of precision. |
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| Keywords:On-Line Sketch Recognition, Adaptation, Support Vector Machines, Incremental Learning |
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