Democratic co-learning

被引：0

作者：

Zhou, Y ^{[1
]}

Goldman, S ^{[1
]}

机构：

[1] Univ S Alabama, Sch Comp & Informat Sci, Mobile, AL 36688 USA

来源：

ICTAI 2004: 16TH IEEE INTERNATIONALCONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2004年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For many machine learning applications it is important to develop algorithms that use both labeled and unlabeled data. We present democratic co-learning in which multiple algorithms instead of multiple views enable learners to label data for each other Our technique leverages off the fact that different learning algorithms have different inductive biases and that better predictions can be made by the voted majority. We also present democratic priority sampling, a new example selection method for active learning.

引用

页码：594 / 602

页数：9

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