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 fea-ture of different users and cast away the special user's character, so this method can adapt the different users withoutover fitting. In this paper, the repetitive learning strategy and other two incremental learning algorithms are presentedfor comparison. Based on theoretical analysis and experimental results, we draw the conclusion that SVM-based incre-mental learning can solve the user conflict problem.
A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.
