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检索条件"主题词=Multi-instance Multi-label Learning"
57 条 记 录,以下是11-20 订阅
排序:
Predicting multiple Functions of Sustainable Flood Retention Basins under Uncertainty via multi-instance multi-label learning
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WATER 2015年 第4期7卷 1359-1377页
作者: Yang, Qinli Boehm, Christian Scholz, Miklas Plant, Claudia Shao, Junming Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Chengdu 611731 Peoples R China Univ Munich Inst Comp Sci D-80937 Munich Germany Univ Salford Sch Comp Sci & Engn Civil Engn Res Grp Salford M5 4WT Lancs England Helmholtz Zentrum Munich German Res Ctr Environm Hlth D-85764 Neuherberg Germany
The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to... 详细信息
来源: 评论
MIMLRBF: RBF neural networks for multi-instance multi-label learning
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NEUROCOMPUTING 2009年 第16-18期72卷 3951-3956页
作者: Zhang, Min-Ling Wang, Zhi-Jian Hohai Univ Coll Comp & Informat Engn Nanjing 210098 Peoples R China
In multi-instance multi-label learning (MIML), each example is not only represented by multiple instances but also associated with multiple class labels. Several learning frameworks, such as the traditional supervised... 详细信息
来源: 评论
Genome-Wide Protein Function Prediction through multi-instance multi-label learning
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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014年 第5期11卷 891-902页
作者: Wu, Jian-Sheng Huang, Sheng-Jun Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210046 Jiangsu Peoples R China
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains toget... 详细信息
来源: 评论
Bayesian multi-instance multi-label learning using Gaussian process prior
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MACHINE learning 2012年 第1-2期88卷 273-295页
作者: He, Jianjun Gu, Hong Wang, Zhelong Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China
multi-instance multi-label learning (MIML) is a newly proposed framework, in which the multi-label problems are investigated by representing each sample with multiple feature vectors named instances. In this framework... 详细信息
来源: 评论
Dynamic Programming for instance Annotation in multi-instance multi-label learning
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017年 第12期39卷 2381-2394页
作者: Pham, Anh T. Raich, Raviv Fern, Xiaoli Z. Oregon State Univ Sch EECS Corvallis OR 97331 USA
labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are the... 详细信息
来源: 评论
Automatic Waveform Recognition of Overlapping LPI Radar Signals Based on multi-instance multi-label learning
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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1275-1279页
作者: Pan, Zesi Wang, Shafei Zhu, Mengtao Li, Yunjie Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Northern Inst Elect Equipment Beijing 100091 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China
In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receivin... 详细信息
来源: 评论
Transductive multi-instance multi-label learning algorithm with application to automatic image annotation
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EXPERT SYSTEMS WITH APPLICATIONS 2010年 第1期37卷 661-670页
作者: Feng, Songhe Xu, De Beijing Jiaotong Univ Inst Comp & Informat Technol Beijing Peoples R China
Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping and the sca... 详细信息
来源: 评论
Fast multi-instance multi-label learning
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019年 第11期41卷 2614-2627页
作者: Huang, Sheng-Jun Gao, Wei Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China
In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multip... 详细信息
来源: 评论
Person re-identification based on multi-instance multi-label learning
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NEUROCOMPUTING 2016年 217卷 19-26页
作者: Lin, Ying Guo, Feng Cao, Liujuan Wang, Jinlin Xiamen Univ Sch Informat Sci & Engn Dept Cognit Sci Xiamen Peoples R China Xiamen Univ Sch Informat Sci & Engn Dept Comp Sci Xiamen Peoples R China
The person re-identification is an active research branch of the computer vision and attracts many researchers study on it. However, because of the variance in viewpoints, illumination, pedestrians' pose and some ... 详细信息
来源: 评论
instance-BASED K-NEAREST NEIGHBOR ALGORITHM FOR multi-instance multi-label learning
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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2014年 第5期10卷 1861-1871页
作者: Peng, Peng Xu, Xinshun Wang, Xiaolin Shandong Univ Sch Comp Sci & Technol 1500 Shunhua Rd Jinan 250101 Shandong Peoples R China
multi-instance multi-label learning (MIML) is proposed to tackle the problem represented by a bag of instances and associated with multiple labels which appears in a wide range of real-world tasks. Transforming the MI... 详细信息
来源: 评论