Results 11 to 20 of about 101,283 (263)
Correlated Label Propagation with Application to Multi-label Learning [PDF]
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006 Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multi-label learning where a given image can be tagged with multiple class labels.
Feng Kang +2 more
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Exploiting Multi-Label Correlation in Label Distribution Learning
CoRR, 2023 Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation ...
Zhiqiang Kou +3 more
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Multi-Label Feature Selection Based on High-Order Label Correlation Assumption
Entropy, 2020 Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning.
Ping Zhang +3 more
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Multi-Label Feature Selection Based on Min-Relevance Label
IEEE Access, 2023 Multi-label feature selection has been widely adopted to address multi-label data with high-dimension features. It is critical to calculate label correlations for multi-label feature selection.
Wanfu Gao, Hanlin Pan
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An Efficient Stacking Model of Multi-Label Classification Based on Pareto Optimum
IEEE Access, 2019 Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among
Wei Weng +4 more
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Multi-Label Learning with Global and Local Label Correlation [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2018 It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset.
Yue Zhu 0001 +2 more
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MULTI-LABEL RANKING METHOD BASED ON POSITIVE CLASS CORRELATIONS
Jordanian Journal of Computers and Information Technology, 2020 Multi-label classification is a general type of classification that has attracted many researchers in the last two decades due to its applicability to many modern domains, such as scene classification, bioinformatics and text classification, among others.
Raed Alazaidah +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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BundleNet: Learning with Noisy Label via Sample Correlations
IEEE Access, 2018 Sequential patterns are important, because they can be exploited to improve the prediction accuracy of our classifiers. Sequential data, such as time series/video frames, and event data are becoming more and more ubiquitous in a wide spectrum of ...
Chenghua Li +5 more
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Mathematics, 2022 In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions.
Qiang Li, Chang Liu, Qingxin Guo
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