A Class-Incremental Learning Method Based on One Class Support Vector Machine

Abstract

Class-incremental learning refers to the problem that the number of classes increases dynamically in the training stage. In this paper, a method based on one class support vector machine (OCSVM) is proposed for class incremental learning. The basic idea of the proposed method is that one class model is used to learn the distribution of new class, and then the knowledge learned and previous models are used for class incremental learning. The proposed method does not need to store all training data and makes full use of the learned information, so it has lower memory cost and time consumption. Also, the proposed method can build model when only one class is available. Compared with OC2MC, CIL, NN, the proposed method also achieves competitive results on different datasets.