Results 11 to 20 of about 637,915 (182)
Compact learning for multi-label classification [PDF]
Pattern Recognition, 2021 Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC encourages a popular framework named label compression (LC) for capturing label dependency with dimension reduction.
Jiaqi Lv +5 more
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Multi‐label learning based target detecting from multi‐frame data
IET Image Processing, 2021 In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a
Mengqing Mei, Fazhi He
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Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [PDF]
Jisuanji kexue, 2022 Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of ...
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
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Privileged Multi-label Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performance on examples can receive the ...
You, Shan +4 more
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Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
Jisuanji kexue yu tansuo, 2021 RNA-binding protein (RBP) is a total name of a class of proteins that bind to RNA (ribonucleic acid) along with the process of RNA??s regulation metabolic.
YANG Haitao, DENG Zhaohong, WANG Shitong
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Generative Multi-Label Zero-Shot Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Accepted by TPAMI: https://ieeexplore.ieee.org/document ...
Akshita Gupta +5 more
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Multi-Label Weighted Contrastive Cross-Modal Hashing
Applied Sciences, 2023 Due to the low storage cost and high computation efficiency of hashing, cross-modal hashing has been attracting widespread attention in recent years. In this paper, we investigate how supervised cross-modal hashing (CMH) benefits from multi-label and ...
Zeqian Yi +5 more
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ProPML: Probability Partial Multi-label Learning
2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023 Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In this paper, we introduce \our{}, a novel probabilistic approach to this problem that extends the binary cross entropy to the PML setup.
Struski, Łukasz +3 more
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Improving Multi-Label Learning by Correlation Embedding
Applied Sciences, 2021 In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other.
Jun Huang +4 more
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Multi-Target Rough Sets and Their Approximation Computation with Dynamic Target Sets
Information, 2022 Multi-label learning has become a hot topic in recent years, attracting scholars’ attention, including applying the rough set model in multi-label learning.
Wenbin Zheng, Jinjin Li, Shujiao Liao
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