Improving Trained LS-SVM Performance with New Available Data

摘要

Learning is obtaining an underlying rule by using training data sampled from the environment. In many practical situations in inductive learning al-gorithms, it is often expected to further improve the generalization capability after the learning process has been completed if new data are available. One of the common approaches is to add training data to the learning algorithm and retrain it, but re-training for each new data point or data set can be very expensive. In view of the learning methods of human beings, it seems natural to build posterior learning results upon prior results. Firstly, in this paper, we proposed an updating procedure for least square support vector machine(LS{SVM). If initial concept would be built up by LS{SVM inductive algorithm, then concept updated is the normal so-lution corresponding to the initial concept learned.Secondly, we discuss a general framework for up-dating learned concept. Finally, we illustrate the updating method and evaluate it on toy data and real data, their results show that the performance after updating is improved and almost equal to the performance of LS{SVM retrained on whole data.