.png){kind=icon}
返回
Multi-Task Multi-View Learning Based on Cooperative Multi-Objective Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2777888.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traditional multi-task multi-view (MTMV) models work under the single-objective learning framework and cannot incorporate too many regularization terms, which are primarily attributed to the utilization of the conventional numerical optimization methods. To this end, a cooperative multi-objective MTMV (CMO-MTMV) learning method is proposed in this paper. In CMO-MTMV, the MTMV problem is formulated as a multi-objective optimization problem. Compared with the existing single-objective MTMV learning methods, the proposed CMO-MTMV method integrates more relations, including task-task, view-view, instance-instance, and feature-feature relations as multiple objectives. An effective cooperative multi-objective quantum-behaved particle swarm optimization (CMOQPSO) algorithm is further developed to solve the multi-objective optimization problem. The integration of a multi-swarm scheme and a local communication strategy in CMOQPSO renders this algorithm efficient. The experimental results verify the superiority of the proposed CMO-MTMV method compared with the several state-of-the-art machine-learning methods.
Keyword:
Multi-task multi-view learning
multi-objective optimization
quantum-behaved particle swarm optimization
multi-swarm strategy
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:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Multi-Task Diagnosis for Autism Spectrum Disorders Using Multi-Modality Features: A Multi-Center Study.png)
HUMAN BRAIN MAPPINGOAAI
HUMAN BRAIN MAPPINGIF3.3
OAAI分享收藏Facile synthesis of α-MnO2 nanorods at low temperature and their microwave absorption properties
PREAI
分享收藏分享收藏
查看更多内容