Learning with Average Top-k Loss
Fan, Yanbo1,3,4; Lyu, Siwei1; Ying, Yiming2; Hu, Bao-Gang3,4
2017
会议名称Neural Information Processing Systems (NIPS)
会议日期2017
会议地点Long Beach, CA, USA
摘要
In this work, we introduce the average top-k (ATk) loss as a new aggregate loss for supervised learning, which is the average over the k largest individual losses over a training dataset. We show that the ATk loss is a natural generalization of the two widely used aggregate losses, namely the average loss and the maximum loss, but can combine their advantages and mitigate their drawbacks to better adapt to different data distributions. Furthermore, it remains a convex function over all individual losses, which can lead to convex optimization problems that can be solved effectively with conventional gradient-based methods. We provide an intuitive interpretation of the ATk loss based on its equivalent effect on the continuous individual loss functions, suggesting that it can reduce the penalty on correctly classified data. We further give a learning theory analysis of MATk learning on the classification calibration of the ATk loss and the error bounds of ATk-SVM. We demonstrate the applicability of minimum average top-k learning for binary classification and regression using synthetic and real datasets.
关键词Supervised Learning Aggregate Loss Average Top-k
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19993
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Lyu, Siwei
作者单位1.Department of Computer Science, University at Albany, SUNY
2.Department of Mathematics and Statistics, University at Albany, SUNY
3.National Laboratory of Pattern Recognition, CASIA
4.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Fan, Yanbo,Lyu, Siwei,Ying, Yiming,et al. Learning with Average Top-k Loss[C],2017.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Learning with Averag(532KB)会议论文 开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Fan, Yanbo]的文章
[Lyu, Siwei]的文章
[Ying, Yiming]的文章
百度学术
百度学术中相似的文章
[Fan, Yanbo]的文章
[Lyu, Siwei]的文章
[Ying, Yiming]的文章
必应学术
必应学术中相似的文章
[Fan, Yanbo]的文章
[Lyu, Siwei]的文章
[Ying, Yiming]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Learning with Average Top-k Loss.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。