评估开放式类别学习算法的实验协议

Aneesh Chauhan, L. Lopes
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引用次数: 4

摘要

机器学习的各个子领域一直在稳步增长,重点是开发以开放式方式学习的系统。这在语言基础和数据流学习领域尤其明显。这些系统旨在随着新数据的到来而发展,修改和调整已学习的类别,以及适应新的类别。开放式学习虽然具有一些增量学习的特征,但后者不能被定性为标准的增量学习。本文提出并讨论了开放式学习的关键特征,并将其与标准的增量式学习方法区分开来。本文的主要贡献在于对这些算法的评价。通常,学习算法的性能是使用传统的训练测试方法来评估的,如保留、交叉验证等。这些评估方法不适合环境和任务可以改变的应用,因此学习系统经常面临新的类别。为了解决这个问题,提出了一个定义良好且实用的协议。通过评估和比较开放式视觉类别学习任务中的一组学习算法,证明了该协议的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An experimental protocol for the evaluation of open-ended category learning algorithms
There has been a steady surge in various sub-fields of machine learning where the focus is on developing systems that learn in an open-ended manner. This is particularly visible in the fields of language grounding and data stream learning. These systems are designed to evolve as new data arrive, modifying and adjusting learned categories, as well as, accommodating new categories. Although some of the features of incremental learning are present in open-ended learning, the latter can not be characterized as standard incremental learning. This paper presents and discusses the key characteristics of open-ended learning, differentiating it from the standard incremental approaches. The main contribution of this paper is concerned with the evaluation of these algorithms. Typically, the performance of learning algorithms is assessed using traditional train-test methods, such as holdout, cross-validation etc. These evaluation methods are not suited for applications where environments and tasks can change and therefore the learning system is frequently facing new categories. To address this, a well defined and practical protocol is proposed. The utility of the protocol is demonstrated by evaluating and comparing a set of learning algorithms at the task of open-ended visual category learning.
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