  When dealing with multi-class classification problems, it is common practice
to build a model consisting of a series of binary classifiers using a learning
paradigm which dictates how the classifiers are built and combined to
discriminate between the individual classes. As new data enters the system and
the model needs updating, these models would often need to be retrained from
scratch. This work proposes three learning paradigms which allow trained models
to be updated without the need of retraining from scratch. A comparative
analysis is performed to evaluate them against a baseline. Results show that
the proposed paradigms are faster than the baseline at updating, with two of
them being faster at training from scratch as well, especially on larger
datasets, while retaining a comparable classification performance.
