.png){kind=icon}
返回
Learning with privileged information for multi-Label classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2018.03.033.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a novel approach for learning multi-label classifiers with the help of privileged information. Specifically, we use similarity constraints to capture the relationship between available information and privileged information, and use ranking constraints to capture the dependencies among multiple labels. By integrating similarity constraints and ranking constraints into the learning process of classifiers, the privileged information and the dependencies among multiple labels are exploited to construct better classifiers during training. A maximum margin classifier is adopted, and an efficient learning algorithm of the proposed method is also developed. We evaluate the proposed method on two applications: multiple object recognition from images with the help of implicit information about object importance conveyed by the list of manually annotated image tags; and multiple facial action unit detection from low-resolution images augmented by high-resolution images. Experimental results demonstrate that the proposed method can effectively take full advantage of privileged information and dependencies among multiple labels for better object recognition and better facial action unit detection. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Privileged information
Multi-label classification
Similarity constraints
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:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
An extensive experimental comparison of methods for multi-label learning.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏A novel extreme learning machine using privileged information一种利用特权信息的新型极限学习机NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Enhancing multi-label classification by modeling dependencies among labelsPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏ML-KNN: A lazy learning approach to multi-label leamingMl-knn: 一种多标签学习的懒惰学习方法PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容