An online support vector machine for the open-ended environment

Abstract

Support Vector Machine (SVM) has been widely applied in expert systems. Traditional SVM works as a closed system, which means the parameters of the SVM freeze after training is finished. This strategy leads to the SVM cannot adapt to the online learning environment and also hinders the application of the SVM in expert systems which need to be updated in an online way. Many online SVMs retrain SVM with the current support vectors and new incoming data. However, the retained system usually shows a declined performance due to losing much information about the data. To handle this problem, we propose an Online Support Vector Machine (OSVM). The OSVM includes a Representative Prototype Area (RPA), which retains the representative data of all historical data. In the RPA, each class is maintained by an Online Incremental Feature Map (OIM) which learns a suitable representative set from the stream data automatically, e.g., we do not need to predefine a compression ratio, the OIM will dynamically adapt to the current input data. Retraining is periodically conducted on the updated OIM, which can avoid a declined performance and significantly reduce the computational cost. Variety data sets from different fields are used to test our method and the experimental results demonstrate that the accuracy of OSVM is comparable to that of the state-of-the-art SVM and the speed of OSVM is much faster. Moreover, OSVM is more stable than the other methods in the open-ended learning environment, where stream data from novel classes occur. This implies the OSVM has a good application prospect in online learning expert systems.