.png){kind=icon}
返回
Towards a unified multi-source-based optimization framework for multi-label learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2018.12.016.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the era of Big Data, a practical yet challenging task is to make learning techniques more universally applicable in dealing with the complex learning problem, such as multi-source multi-label learning. While some of the early work have developed many effective solutions for multi-label classification and multi-source fusion separately, in this paper we learn the two problems together, and propose a novel method for the joint learning of multiple class labels and data sources, in which an optimization framework is constructed to formulate the learning problem, and the result of multi-label classification is induced by the weighted combination of the decisions from multiple sources. The proposed method is responsive in exploiting the label correlations and fusing multi-source data, especially in the fusion of long-tail data. Experiments on various multi-source multi-label data sets reveal the advantages of the proposed method. (C) 2018 Published by Elsevier B.V.
Keyword:
Multi-label learning
Multi-source fusion
Optimization framework
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification用于多标签分类的学习标签特定特征和类别相关标签
PREAI
分享收藏PubChem: a public information system for analyzing bioactivities of small moleculesPubChem: 用于分析小分子生物活性的公共信息系统.png)
NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏分享收藏Morphology and properties of melt-spun polycarbonate fibers containing single- and multi-wall carbon nanotubesPolymer
PolymerIF0
PREAI
分享收藏
查看更多内容