Incremental learning is a machine learning paradigm where the learning process takes place whenever new example/s emerge and adjusts what has been learned according to the new example/s. The most prominent difference of incremental learning from traditional machine learning is that it does not assume the availability of a sufficient training set before the learning process, but the training examples appear over time. In this paper we discuss the methods of incremental learning which are currently available. This paper gives the overview of the current research in the incremental learning which will be beneficial to the research scalars.
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