论文标题
在课堂订购中进行增量学习
On Class Orderings for Incremental Learning
论文作者
论文摘要
班级顺序在评估增量学习中的影响很少受到关注。在本文中，我们调查了类订单对逐步学习的分类器的影响。我们提出了一种计算数据集各种顺序的方法。订单是通过从混淆矩阵中模拟退火优化来得出的，并反映了不同的增量学习方案，包括最大和最小的混乱任务。我们在提议的订单上评估了广泛的最先进的增量学习方法。结果表明，订购可能会对方法的性能和方法的排名产生重大影响。
The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods.
