.png){kind=icon}
返回
Distributed multi-task classification: a decentralized online learning approach
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-017-5676-y.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Although dispersing one single task to distributed learning nodes has been intensively studied by the previous research, multi-task learning on distributed networks is still an area that has not been fully exploited, especially under decentralized settings. The challenge lies in the fact that different tasks may have different optimal learning weights while communication through the distributed network forces all tasks to converge to an unique classifier. In this paper, we present a novel algorithm to overcome this challenge and enable learning multiple tasks simultaneously on a decentralized distributed network. Specifically, the learning framework can be separated into two phases: (i) multi-task information is shared within each node on the first phase; (ii) communication between nodes then leads the whole network to converge to a common minimizer. Theoretical analysis indicates that our algorithm achieves a regret bound when compared with the best classifier in hindsight, which is further validated by experiments on both synthetic and real-world datasets.
Keyword:
Decentralized distributed learning
Multi-task learning
Online learning
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
IF:
2.9
论文数:
2.7K
被引数:
3.4W
机构
引用论文
.png){kind=icon}
Substance P in an argentaffin carcinoid of the caecum: Biochemical and biological characterization
PREAI
分享收藏
查看更多内容