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31-40 订阅
排序:
An multi-instance multi-label learning Method for Predicting Protein Function in the Yeast Genome
An Multi-instance Multi-label Learning Method for Predicting...
收藏 引用
第六届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会
作者: 吴建盛 汤丽华 韩微 晏善成 地理与生物信息学院 南京邮电大学南京210046 地理与生物信息学院 南京邮电大学南京210046 生物电子学国家重点实验室 生物科学与医学工程学院东南大学南京210096
Background: Understanding biological functions of proteins is a key challenge in the post-genomic *** to its inherent difficulty and expense, experimental annotation of protein functions cannot scale up to accommodate... 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
multi-instance multi-label distance metric learning for genome-wide protein function prediction
收藏 引用
COMPUTATIONAL BIOLOGY AND CHEMISTRY 2016年 63卷 30-40页
作者: Xu, Yonghui Min, Huaqing Song, Hengjie Wu, Qingyao South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China
multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also m... 详细信息
来源: 评论
Improvement of learning Algorithm for the multi-instance multi-label RBF Neural Networks Trained with Imbalanced Samples
收藏 引用
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2013年 第4期29卷 765-776页
作者: Li, Cunhe Shi, Guoqiang China Univ Petr Coll Comp & Commun Engn Qingdao 266555 Peoples R China
multi-instance multi-label learning (MIML) is a novel learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the multi-inst... 详细信息
来源: 评论
Partial multi-label learning With Noisy label Identification
收藏 引用
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年 第7期44卷 3676-3687页
作者: Xie, Ming-Kun Huang, Sheng-Jun Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Pattern Anal & Machine Intelligence Collaborat Innovat Ctr Novel Software Technol & I Nanjing 211106 Peoples R China
Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML proble... 详细信息
来源: 评论
multi-task MIML learning for pre-course student performance prediction
收藏 引用
Frontiers of Computer Science 2020年 第5期14卷 113-121页
作者: Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China
In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning ***,given the large size of a course’s students at universities,it has... 详细信息
来源: 评论
Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method
收藏 引用
NEURAL COMPUTING & APPLICATIONS 2013年 第7-8期22卷 1563-1569页
作者: Li, Cunhe Shi, Guoqiang China Univ Petr Coll Comp & Commun Engn Qingdao 266555 Peoples R China
multi-instance multi-label learning (MIML) is an innovative learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the mult... 详细信息
来源: 评论
multi-instance multi-label image classification: A neural approach
收藏 引用
NEUROCOMPUTING 2013年 99卷 298-306页
作者: Chen, Zenghai Chi, Zheru Fu, Hong Feng, Dagan Hong Kong Polytech Univ Elect & Informat Engn Dept Ctr Multimedia Signal Proc Kowloon Hong Kong Peoples R China Chu Hai Coll Higher Educ Dept Comp Sci Tsuen Wan Hong Kong Peoples R China Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Sydney Inst Biomed Engn & Technol Sydney NSW 2006 Australia
In this paper, a multi-instance multi-label algorithm based on neural networks is proposed for image classification. The proposed algorithm, termed multi-instance multi-label neural network (MIMLNN), consists of two s... 详细信息
来源: 评论
A fast Markov chain based algorithm for MIML learning
收藏 引用
NEUROCOMPUTING 2016年 216卷 763-777页
作者: Ng, Michael K. Wu, Qingyao Shen, Chenyang Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Guangdong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China
multi-instance multi-label (MIML) learning is one of challenging research problems in machine learning. In the literature, there are several methods for solving MIML problems. However, they may take a long computation... 详细信息
来源: 评论
Incomplete label multiple instance multiple label learning
收藏 引用
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年 第3期44卷 1320-1337页
作者: Tam Nguyen Raich, Raviv Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA
With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings hav... 详细信息
来源: 评论