An Outlier Detection Method and its Application to Multicore-Chip Power Estimation

doi:10.1109/ASICON.2017.8252513

	An Outlier Detection Method and its Application to Multicore-Chip Power Estimation
	Li, Yaguang; Zhou, Pingqiang
	2017
会议录名称	2017 IEEE 12TH INTERNATIONAL CONFERENCE ON ASIC (ASICON)
卷号	2017-October
页码	460-463
发表状态	已发表
DOI	10.1109/ASICON.2017.8252513
摘要	Machine learning has been recently applied to solve challenging research problems in the EDA area. The performance of the machine learning algorithms are vulnerable to effects such as the outliers and the insignificant features in the input training data set. In this paper, we propose a model averaging method, together with an outlier detection method, to make the machine learning process more robust to such contaminating effects. Results on artificial and chip power estimation data sets show that our method behave much better than the conventional ordinary least square method which is widely used in the machine learning community.
关键词	Mathematical model Training Data models Predictive models Anomaly detection Uncertainty Estimation
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	Guiyang
会议日期	25-28 Oct. 2017
URL	查看原文
收录类别	CPCI ; EI
语种	英语
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000426983400117
出版者	IEEE
EI入藏号	20181404981187
EI主题词	Artificial intelligence ; Data handling ; Learning algorithms ; Learning systems ; Statistics
EI分类号	Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Numerical Methods:921.6 ; Mathematical Statistics:922.2
原始文献类型	Proceedings Paper
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/18234
专题	信息科学与技术学院信息科学与技术学院_PI研究组_周平强组信息科学与技术学院_硕士生
通讯作者	Li, Yaguang
作者单位	ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Li, Yaguang,Zhou, Pingqiang. An Outlier Detection Method and its Application to Multicore-Chip Power Estimation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:460-463.