【文件属性】：
文件名称：Theory of Active Learning
文件大小：1.53MB
文件格式：PDF
更新时间：2022-01-28 19:30:29
主动学习 机器学习
Active learning is a protocol for supervised machine learning, in which
a learning algorithm sequentially requests the labels of selected data
points from a large pool of unlabeled data. This contrasts with passive
learning, where the labeled data are taken at random. The objective in
active learning is to produce a highly-accurate classifier, ideally using
fewer labels than the number of random labeled data sufficient for passive
learning to achieve the same. This article describes recent advances
in our understanding of the theoretical benefits of active learning, and
implications for the design of effective active learning algorithms. Much
of the article focuses on a particular technique, namely disagreementbased
active learning, which by now has amassed a mature and coherent
literature. It also briefly surveys several alternative approaches from
the literature. The emphasis is on theorems regarding the performance
of a few general algorithms, including rigorous proofs where appropriate.
However, the presentation is intended to be pedagogical, focusing
on results that illustrate fundamental ideas, rather than obtaining the
strongest or most general known theorems. The intended audience includes
researchers and advanced graduate students in machine learning
and statistics, interested in gaining a deeper understanding of the recent
and ongoing developments in the theory of active learning.