Results 11 to 20 of about 273,362 (259)
HML-RF: Hybrid Multi-Label Random Forest
IEEE Access, 2022 Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels. In this, labels are correlated, and hence label dependency information plays a vital role.
Vikas Jain +2 more
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K-Nearest Neighbor Multi-Label Learning Based on Label Correlation [PDF]
Jisuanji gongcheng, 2022 Multi-label learning is a popular research topic in the field of machine learning.It can effectively solve multi-lingualism in the real world.In multi-label learning, a certain correlation exists between multiple labels of the sample.Ignoring the ...
QIAN Long, ZHAO Jing, HAN Jingyu, MAO Yi
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Label distribution learning via label correlation grid
CoRR, 2022 Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artificial or environmental factors.
Qimeng Guo +3 more
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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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Double Attention for Multi-Label Image Classification
IEEE Access, 2020 Multi-label image classification is an essential task in image processing. How to improve the correlation between labels by learning multi-scale features from images is a very challenging problem.
Haiying Zhao +3 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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Fast Extended One-Versus-Rest Multi-Label Support Vector Machine Using Approximate Extreme Points
IEEE Access, 2017 Existing extended one-versus-rest multi-label support vector machine (OVR-ESVM) adopting non-linear kernel is seriously restricted by excessive training time when it is applied to large-scale data set.
Zhongwei Sun +5 more
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IEEE Access, 2020 The ability to extract image features largely determines the accuracy of image classification. However, external interferences in images such as translation, rotation, scaling, occlusion, light, and non-linear deformation, result in greater intra-class ...
Songshang Zou +3 more
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Label Correlation Guided Deep Multi-View Image Annotation
IEEE Access, 2019 Automatic image annotation is an important technique which has been widely applied in many fields such as social network image analysis and retrieval, face recognition and so on.
Zhe Xue +4 more
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COS-LDL: Label Distribution Learning by Cosine-Based Distance-Mapping Correlation
IEEE Access, 2020 Label distribution learning (LDL) is a popular research trend in multi-label learning. Competing methods have been designed to improve the predictive performance. In this paper, we propose a method called cosine-based correlation for LDL (COS-LDL).
Heng-Ru Zhang +3 more
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