具有性能保证的演化任务的极小极大前向与后向学习
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
AI总结：
本文提出增量极小极大风险分类器(IMRCs)，通过前向与后向学习有效处理随时间演化的分类任务，并在理论上证明了其性能提升与任务预期二次变化和数量相关，实验显示在样本量减少时提升尤为显著。
摘要：
对于随时间到达的一系列分类任务，任务通常是演化的，即连续任务往往具有更高的相似性。通过对不断增长的任务序列进行增量学习，有望利用序列中所有任务的信息（前向与后向学习），即使在每个任务样本量很少的情况下也能实现准确分类。然而，为持续学习和概念漂移适应开发的现有技术，要么是为具有时间独立相似性的任务设计的，要么仅旨在学习序列中的最后一个任务。本文提出了增量极小极大风险分类器（IMRCs），能有效利用前向与后向学习并兼顾演化任务。此外，我们从分析角度刻画了前向与后向学习在任务的预期二次变化和任务数量方面所提供的性能提升。实验评估表明，IMRCs能带来显著的性能提升，尤其是在样本量减少的情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
