期刊
ACM COMPUTING SURVEYS
卷 47, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2716262
关键词
Algorithms; Experimentation; Theory; Multilabel learning; ranking; classification; machine learning; data mining
资金
- Ministry of Science and Technology project [TIN-2011-22408]
Multilabel learning has become a relevant learning paradigm in the past years due to the increasing number of fields where it can be applied and also to the emerging number of techniques that are being developed. This article presents an up-to-date tutorial about multilabel learning that introduces the paradigm and describes the main contributions developed. Evaluation measures, fields of application, trending topics, and resources are also presented.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据