Results 31 to 40 of about 637,915 (182)
Expede Herculem: Learning Multi Labels From Single Label
IEEE Access, 2018 Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set.
Dejun Mu +5 more
doaj +1 more source
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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A Multi-Label Learning Method Using Affinity Propagation and Support Vector Machine
IEEE Access, 2017 Multi-label learning plays a critical role in the areas of data mining, multimedia, and machine learning. Although many multi-label approaches have been proposed, few of them have considered to de-emphasize the effect of noisy features in the learning ...
Jing-Jing Li +3 more
doaj +1 more source
Multi-View Multi-Label Learning With View-Label-Specific Features
IEEE Access, 2019 In multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are ...
Jun Huang +5 more
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An Incremental Kernel Extreme Learning Machine for Multi-Label Learning With Emerging New Labels
IEEE Access, 2020 Multi-label learning with emerging new labels is a practical problem that occurs in data streams and has become an important new research issue in the area of machine learning.
Yanika Kongsorot +2 more
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Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces [PDF]
, 2018 We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary ...
Augenstein, Isabelle +2 more
core +2 more sources
Multi-label Ensemble Learning [PDF]
, 2011 Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners.
Chuan Shi +3 more
openaire +1 more source
Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis
Applied Sciences, 2018 Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training
Wenjun Bai, Changqin Quan, Zhiwei Luo
doaj +1 more source
Learning multi-label scene classification [PDF]
Pattern Recognition, 2004 In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive.
Matthew R. Boutell +3 more
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Multi-Label Learning With Label Specific Features Using Correlation Information
IEEE Access, 2019 To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years.
Huirui Han +4 more
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