.png){kind=icon}
返回
Parallel multi-graph classification using extreme learning machine and MapReduce
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2016.03.111.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A multi-graph is represented by a bag of graphs and modeled as a generalization of a multi-instance. Multi-graph classification is a supervised learning problem, which has a wide range of applications, such as scientific publication categorization, bio-pharmaceutical activity tests and online product recommendation. However, existing algorithms are limited to process small datasets due to high computation complexity of multi-graph classification. Specially, the precision is not high enough for a large dataset. In this paper, we propose a scalable and high-precision parallel algorithm to handle the multi-graph classification problem on massive datasets using MapReduce and extreme learning machine. Extensive experiments on real-world and synthetic graph datasets show that the proposed algorithm is effective and efficient. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Multi-graph
Classification
Extreme learning machine
MapReduce
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
Desalination
OAAI
DesalinationIF0
OAAI分享收藏On extending extreme learning machine to non-redundant synergy pattern based graph classificationNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏An OS-ELM based distributed ensemble classification framework in P2P networksNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容