.png){kind=icon}
返回
Building personal maps from GPS data
.png){kind=icon}
PRE
AI
DOI：10.1196/annals.1382.017.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article we discuss an assisted cognition information technology system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a generative dynamic Bayesian network is used to learn transportation routines, and infer goals and potential user errors at real time. We focus on the basic structures of the models and briefly discuss the inference and learning techniques. Experiments show that our system is able to accurately extract and label places, predict the goals of a person, and recognize situations in which the user makes mistakes, such as taking a wrong bus.
Keyword:
personal map
GPS
Relational Markov Network (RMN)
dynamic Bayesian Network (DBN)
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
暂无期刊信息
机构
暂无机构信息
引用论文
.png){kind=icon}
没有更多内容
没有更多内容