BriefGPT.xyz
Apr, 2020
定期使用来自最终用户反馈的数据重新训练分类器以过滤掉一些不值得依赖的人
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
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Joshua Lockhart, Samuel Assefa, Tucker Balch, Manuela Veloso
TL;DR
本文通过多智能体的观点，考虑了用户反馈在文本分类中的影响，并介绍了一种分类器，该分类器可以了解哪些用户不可靠并过滤他们的反馈，从而提高后续迭代的性能。
Abstract
document classification
is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing
feedback-retrain loop
with the team that maintain it. We consider this
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