  A method based on one class support vector machine (OCSVM) is proposed for
class incremental learning. Several OCSVM models divide the input space into
several parts. Then, the 1VS1 classifiers are constructed for the confuse part
by using the support vectors. During the class incremental learning process,
the OCSVM of the new class is trained at first. Then the support vectors of the
old classes and the support vectors of the new class are reused to train 1VS1
classifiers for the confuse part. In order to bring more information to the
certain support vectors, the support vectors are at the boundary of the
distribution of samples as much as possible when the OCSVM is built. Compared
with the traditional methods, the proposed method retains the original model
and thus reduces memory consumption and training time cost. Various experiments
on different datasets also verify the efficiency of the proposed method.
