UrbanWater Quality Prediction based on Multi-task Multi-view Learning
文献类型：会议论文
| 作者 | Ye Liu; Yu Zheng; Yuxuan Liang; Shuming Liu; David S. Rosenblum |
| 出版日期 | 2016 |
| 会议名称 | IJCAI 2016 |
| 会议地点 | New York |
| 英文摘要 | Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. In this work, we forecast the water quality of a station over the next few hours, using a multitask multi-view learning method to fuse multiple datasets from different domains. In particular, our learning model comprises two alignments. The first alignment is the spaio-temporal view alignment, which combines local spatial and temporal information of each station. The second alignment is the prediction alignment among stations, which captures their spatial correlations and performs copredictions by incorporating these correlations. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10318]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Ye Liu,Yu Zheng,Yuxuan Liang,et al. UrbanWater Quality Prediction based on Multi-task Multi-view Learning[C]. 见:IJCAI 2016. New York. |
入库方式： OAI收割
来源：深圳先进技术研究院
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