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For a sequence of classification tasks that arrive over time, it is commonthat tasks are evolving in the sense that consecutive tasks often have a highersimilarity. The incremental learning of a growing sequence of tasks holdspromise to enable accurate classification even with few samples per task byleveraging information from all the tasks in the sequence (forward and backwardlearning). However, existing techniques developed for continual learning andconcept drift adaptation are either designed for tasks with time-independentsimilarities or only aim to learn the last task in the sequence. This paperpresents incremental minimax risk classifiers (IMRCs) that effectively exploitforward and backward learning and account for evolving tasks. In addition, weanalytically characterize the performance improvement provided by forward andbackward learning in terms of the tasks' expected quadratic change and thenumber of tasks. The experimental evaluation shows that IMRCs can result in asignificant performance improvement, especially for reduced sample sizes.

Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees