For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity. The incremental learning of a growing sequence of tasks holds promise to enable accurate classification even with few samples per task by leveraging information from all the tasks in the sequence (forward and backward learning). However, existing techniques developed for continual learning and concept drift adaptation are either designed for tasks with time-independent similarities or only aim to learn the last task in the sequence. This paper presents incremental minimax risk classifiers (IMRCs) that effectively exploit forward and backward learning and account for evolving tasks. In addition, we analytically characterize the performance improvement provided by forward and backward learning in terms of the tasks' expected quadratic change and the number of tasks. The experimental evaluation shows that IMRCs can result in a significant performance improvement, especially for reduced sample sizes.

翻译：针对随时间推移到达的一系列分类任务，任务往往具有演化特性，即连续任务间常呈现更高相似性。通过利用任务序列中所有任务的信息（前向与后向学习），对不断增长的任务序列进行增量学习有望在每任务样本数较少的情况下实现精确分类。然而，现有持续学习与概念漂移适应技术要么针对时间无关相似性任务设计，要么仅旨在学习序列中的最后一个任务。本文提出增量最小最大风险分类器（IMRCs），该分类器能有效利用前向与后向学习并适应演化任务。此外，我们从任务期望二次变化量和任务数量两个角度，解析刻画了前向与后向学习带来的性能提升。实验评估表明，IMRCs能实现显著性能提升，尤其在样本量减少的情况下。