Abstract

User adaptation is critical in the future design of human-computer interaction systems. Many pattern recognition problems, such as handwriting/sketching recognition and speech recognition, are user dependent since different users' handwritings, drawing styles, and accents are different. Hence, the classifiers for solving these problems should provide the functionality of user adaptation so as to let all users experience better recognition results. However, the user adaptation functionality requires the classifiers have the incremental learning ability in order to learn fast. In this paper, an SVM-based incremental active learning algorithm is presented to solve this problem. By utilizing the support vectors and only a small portion of the nonsupport vectors as well, in addition to the new interrogative samples, in the iterative training and reclassification cycle, both the training time and the storage space are saved with only very little classification precision being lost. Theoretical analysis, experimentation, evaluation, and real application samples in our online graphics recognition system are presented to show the effectiveness of this algorithm.

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