咨询与建议

限定检索结果

文献类型

  • 39 篇 期刊文献
  • 17 篇 会议
  • 1 篇 学位论文

馆藏范围

  • 57 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 52 篇 工学
    • 45 篇 计算机科学与技术...
    • 16 篇 电气工程
    • 5 篇 软件工程
    • 2 篇 控制科学与工程
    • 1 篇 电子科学与技术(可...
    • 1 篇 信息与通信工程
    • 1 篇 水利工程
    • 1 篇 环境科学与工程(可...
    • 1 篇 生物医学工程(可授...
    • 1 篇 生物工程
  • 7 篇 理学
    • 5 篇 生物学
    • 2 篇 物理学
    • 1 篇 数学
    • 1 篇 统计学(可授理学、...
  • 5 篇 医学
    • 2 篇 基础医学(可授医学...
    • 2 篇 特种医学
    • 1 篇 临床医学
  • 4 篇 管理学
    • 4 篇 管理科学与工程(可...
  • 3 篇 农学
    • 1 篇 作物学
  • 1 篇 教育学
    • 1 篇 教育学

主题

  • 57 篇 multi-instance m...
  • 8 篇 machine learning
  • 7 篇 protein function...
  • 4 篇 neural networks
  • 4 篇 scene classifica...
  • 3 篇 text categorizat...
  • 3 篇 ensemble learnin...
  • 3 篇 deep learning
  • 3 篇 radial basis fun...
  • 3 篇 feature extracti...
  • 3 篇 training
  • 2 篇 model interpreta...
  • 2 篇 sequence-level
  • 2 篇 multi-label lear...
  • 2 篇 task analysis
  • 2 篇 breast histopath...
  • 2 篇 genome wide
  • 2 篇 image annotation
  • 2 篇 brain ct
  • 2 篇 label correlatio...

机构

  • 5 篇 nanjing univ nat...
  • 4 篇 shandong inst bu...
  • 3 篇 nanjing univ sta...
  • 3 篇 ludong univ sch ...
  • 2 篇 south china univ...
  • 2 篇 shandong univ sc...
  • 2 篇 beijing inst tec...
  • 2 篇 china univ petr ...
  • 2 篇 nanjing univ pos...
  • 2 篇 peng cheng lab p...
  • 2 篇 dalian univ tech...
  • 1 篇 univ sci & techn...
  • 1 篇 nanjing univ sof...
  • 1 篇 south china univ...
  • 1 篇 southwest petr u...
  • 1 篇 hong kong polyte...
  • 1 篇 univ salford sch...
  • 1 篇 univ aizu comp s...
  • 1 篇 georgia inst tec...
  • 1 篇 nanjing univ pos...

作者

  • 5 篇 zhou zhi-hua
  • 4 篇 huang sheng-jun
  • 3 篇 wu qingyao
  • 2 篇 shi guoqiang
  • 2 篇 chen tongtong
  • 2 篇 liu ying
  • 2 篇 yang yang
  • 2 篇 ding xinmiao
  • 2 篇 gu hong
  • 2 篇 liu chan-juan
  • 2 篇 xu xinshun
  • 2 篇 he jianjun
  • 2 篇 min huaqing
  • 2 篇 raich raviv
  • 2 篇 zou hailin
  • 2 篇 li yunjie
  • 2 篇 liu chanjuan
  • 2 篇 chen tong-tong
  • 2 篇 pan zesi
  • 2 篇 wang zhelong

语言

  • 56 篇 英文
  • 1 篇 中文
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:
multi-instance multi-label learning for Image Categorization Based on Integrated Contextual Information  9th
Multi-instance Multi-label Learning for Image Categorization...
收藏 引用
9th International Conference on Image and Graphics (ICIG)
作者: Li, Xingyue Wan, Shouhong Zou, Chang Yin, Bangjie Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China
In image categorization, one image is usually reshaped as a bag of instances affiliated with multiple labels, which naturally induces a paradigm of multi-instance and multi-label learning (MIMLL). Previous researches ... 详细信息
来源: 评论
HMIML: Hierarchical multi-instance multi-label learning of Drosophila Embryogenesis Images Using Convolutional Neural Networks
HMIML: Hierarchical Multi-Instance Multi-Label Learning of <...
收藏 引用
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
作者: Li, Tiange Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China
The Drosophila embryonic gene expression images provide important spatio-temporal expression information for understanding the mechanisms of Drosophila embryogenesis. Automatic annotation of these images is an imperat... 详细信息
来源: 评论
Deep multi-instance multi-label learning for Image Annotation
收藏 引用
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年 第3期32卷 1859005-1859005页
作者: Guo, Hai-Feng Han, Lixin Su, Shoubao Sun, Zhou-Bao Hohai Univ Coll Comp & Informat Nanjing 210024 Jiangsu Peoples R China Jinling Inst Technol Sch Comp Engn Nanjing 211169 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Nanjing Audit Univ Nanjing 211815 Jiangsu Peoples R China
multi-instance multi-label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have fo... 详细信息
来源: 评论
Dynamic Programming for instance Annotation in multi-instance multi-label learning
收藏 引用
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
multi-instance multi-label learning for whole slide breast histopathology  4
Multi-instance multi-label learning for whole slide breast h...
收藏 引用
Conference on Medical Imaging - Digital Pathology
作者: Mercan, Caner Mercan, Ezgi Aksoy, Selim Shapiro, Linda G. Weaver, Donald L. Elmore, Joann G. Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkey Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Univ Vermont Dept Pathol Burlington VT 05405 USA Univ Washington Dept Med Seattle WA 98195 USA
Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis... 详细信息
来源: 评论
Predicting Protein Functions of Bacteria Genomes via multi-instance multi-label Active learning  3
Predicting Protein Functions of Bacteria Genomes via Multi-i...
收藏 引用
3rd IEEE International Conference on Integrated Circuits and Microsystems (ICICM)
作者: Wu, Jiansheng Zhu, Wenyong Jiang, Ye Sun, Guwei Gao, Yusheng Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing Jiangsu Peoples R China
Bacteria are workhorses in the fields of molecular biology, genetics, and biochemistry. Understanding the biological functions of proteins is important for bacteria studies in the post-genomic era. There are a large n... 详细信息
来源: 评论
A New multi-instance multi-label learning approach for image and text classification
收藏 引用
multiMEDIA TOOLS AND APPLICATIONS 2016年 第13期75卷 7875-7890页
作者: Yan, Kaobi Li, Zhixin Zhang, Canlong Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guangxi Expt Ctr Informat Sci Guilin 541004 Peoples R China
Recently, a reasonable and effectively framework to deal with the classification problem of the polysemy object with complex connotation is multi-instance multi-label (MIML) learning framework in which each example is... 详细信息
来源: 评论
A multi-instance multi-label learning algorithm based on instance correlations
收藏 引用
multiMEDIA TOOLS AND APPLICATIONS 2016年 第19期75卷 12263-12284页
作者: Liu, Chanjuan Chen, Tongtong Ding, Xinmiao Zou, Hailin Tong, Yan Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Shandong Inst Business & Technol Sch Informat & Elect Engn Yantai 264005 Peoples R China
Existing multi-instance multi-label learning algorithms generally assume that instances in a bag are independent of each other, which is difficult to be guaranteed in practical applications. A novel multi-instance mul... 详细信息
来源: 评论
Person re-identification based on multi-instance multi-label learning
收藏 引用
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
MIMLTWSVM: Twin Support Vector Machine for multi-instance multi-label learning  11
MIMLTWSVM: Twin Support Vector Machine for Multi-Instance Mu...
收藏 引用
11th International Conference on Industrial and Information Systems (ICIIS)
作者: Tomar, Divya Agarwal, Sonali Indian Inst Informat Technol Allahabad Uttar Pradesh India
Recently, multi instance multi label (MIML) learning has attracted the attention of researchers in which an example not only belongs to multiple instances but also associated with multiple class labels. This study pro... 详细信息
来源: 评论