Results 31 to 40 of about 3,113,449 (308)
Zero‐shot multi‐label learning via label factorisation
IET Computer Vision, 2019 This study considers the zero‐shot learning problem under the multi‐label setting where each test sample is associated with multiple labels that are unseen in training data.
Hang Shao +3 more
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Neural Tensor Network for Multi- Label Classification
IEEE Access, 2019 The difference of multi-label classification from traditional classification is that an instance may associate a set of labels simultaneously. In recent study, some scholars have proposed that the information which derives from the query instance's ...
Wenxing Hong +3 more
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On the consistency of multi-label learning
Artificial Intelligence, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Gao 0008, Zhi-Hua Zhou
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A New Ant Colony Algorithm for Multi-Label Classification with Applications in Bioinformatics [PDF]
, 2006 The conventional classification task of data mining can be called single-label classification, since there is a single class attribute to be predicted.
Chan, Allen +3 more
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Multi-label code-smell dataset
, 2023 The multi-label code-smell dataset for studies related to multi-label ...
Binh Nguyen Thanh (13965222)
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Comparing Multi-Label Classification Methods for Provisional Biopharmaceutics Class Prediction. [PDF]
, 2014 The biopharmaceutical classification system (BCS) is now well established and utilized for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multilabel classification.
Taravat Ghafourian +8 more
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Multi-label LeGo — Enhancing Multi-label Classifiers with Local Patterns [PDF]
, 2012 The straightforward approach to multi-label classification is based on decomposition, which essentially treats all labels independently and ignores interactions between labels. We propose to enhance multi-label classifiers with features constructed from local patterns representing explicitly such interdependencies.
Wouter Duivesteijn +3 more
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Active learning with label correlation exploration for multi‐label image classification
IET Computer Vision, 2017 Multi‐label image classification has attracted considerable attention in machine learning recently. Active learning is widely used in multi‐label learning because it can effectively reduce the human annotation workload required to construct high ...
Jian Wu +5 more
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Alternate Optimization Method for 3D Pulmonary Nodules Retrieval Based on Medical Sign
Taiyuan Ligong Daxue xuebao, 2022 In order to solve the problems such as the complicated process of manual diagnosis and retrieval, high misdiagnosis rate, large amount of data, sparse Hash codes, a 3D ResNet network based on multi-label semantic supervision was proposed to quantify the ...
Yanan ZHANG +4 more
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Multi-Label Ranking: Mining Multi-Label and Label Ranking Data
, 2023 We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art
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