Results 11 to 20 of about 3,113,449 (308)
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management, 2005 Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent.
Ghamrawi, Nadia, McCallum, Andrew
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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 ...
Shan You +4 more
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IEEE AccessThe confusion matrix is the tool commonly used for the evaluation of the performance of a classification algorithm. While the computation of the confusion matrix for multi-class classification follows a well-developed procedure, the common approach for computing the confusion matrix for multi-label classification suffers from the ambiguity related to ...
Damir Krstinic +3 more
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Application of Label Correlation in Multi-Label Classification: A Survey
Applied SciencesMulti-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-
Shan Huang +6 more
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Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
Applied Sciences, 2023 Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years.
Naseer Ahmed Sajid +9 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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Multi-Label Classification with Meta-Label-Specific Features and Q-Learning [PDF]
Control and Optimization in Applied Mathematics, 2021 Classification is a crucial process in data mining, data science, machine learning, and the applications of natural language processing. Classification methods distinguish the correlation between the data and the output classes.
Seyed Hossein Seyed Ebrahimi +2 more
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Automatic multi-label subject indexing in a multilingual environment [PDF]
, 2003 This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset.
Lauser, Boris, Hotho, Andreas
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Governance of Cross-Border Flow of Industry-Specific Data: International Landscape and China's Path [PDF]
中国工程科学There has been a broad consensus on strengthening the supervision of cross-border data flows and promoting international cooperation. However, the importance attached to the governance of cross-border flows of industry-specific data is still insufficient,
Bo Zhang, Xiaoman Liu, He Liu
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Multi-label Classification Using Vector Generalized Additive Model via Cross-Validation
Journal of ICT, 2023 Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or ...
Amri Muhaimin +2 more
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