Gaussian Process Learning for Location-Based Service Data

被引：0

作者：

Ugurel, Ekin ^{[1
]}

Huang, Shuai ^{[2
]}

Chen, Cynthia ^{[1
,2
]}

机构：

[1] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA

[2] Univ Washington, Dept Ind & Syst Engn, Seattle, WA 98195 USA

来源：

2023 ASIA PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE, APSIPA ASC | 2023年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/APSIPAASC58517.2023.10317343

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We develop a Gaussian process regression model to fill gaps in LBS (Location-Based-Service) data. We demonstrate our model's performance in two experiments with real app-based data, in which it outperforms alternative imputation methods.

引用

下载

页码：1204 / 1207

页数：4

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