Abstract
Class-incremental learning (CIL) is a revolutionary framework we develop in this study to address multi-class problems with support vector machines (SVM). Text classifiers built with support for support vector machines (SVMs) can be kept up-to-date with the help of CIL’s two incremental processes. Reusing previously learned classifier models, the CIL only needs to train a single binary sub-classifier and an extra step for feature assortmentonce a new class is introduced. The projections of the vectors onto the relevant subspaces are analyzed using the present classifier. Any text classification method based on binary classification can use CIL as a universal framework for implementation. We found that the CIL-based SVM not only outperformed well-known batch SVM learning strategies like 1-against-rest, 1-against-1, and divide-by-2, but also required much less time to train.
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