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REID (Research and Evaluation in Education), 2021 Nowadays, in the pandemic of COVID-19, e-learning systems have been widely used to facilitate teaching and learning processes between lecturers and students.
Sulis Sandiwarno
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Deep Transfer Metric Learning [PDF]
IEEE Transactions on Image Processing, 2016 Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep
Junlin Hu +3 more
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Metric Learning for Individual Fairness [PDF]
, 2020 There has been much discussion concerning how "fairness" should be measured or enforced in classification. Individual Fairness [Dwork et al., 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it ...
Ilvento, Christina
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Learning Neighborhoods for Metric Learning [PDF]
, 2012 Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original feature space and remain fixed throughout learning. As a result, the learned metric reflects the original neighborhood relations.
Wang Jun +2 more
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Query-Augmented Active Metric Learning [PDF]
Journal of the American Statistical Association, 2022 In this paper we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, while estimating underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and efficient clustering process.
Deng, Yujia +3 more
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Object Tracking With Structured Metric Learning
IEEE Access, 2019 In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning.
Xiaolin Zhao +4 more
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A review on multi-task metric learning
Big Data Analytics, 2018 Distance metric plays an important role in machine learning which is crucial to the performance of a range of algorithms. Metric learning, which refers to learning a proper distance metric for a particular task, has attracted much attention in machine ...
Peipei Yang, Kaizhu Huang, Amir Hussain
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Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature
Sensors, 2021 The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts.
Giacomo Frisoni +3 more
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An Effective Gaze-Based Authentication Method with the Spatiotemporal Feature of Eye Movement
Sensors, 2022 Eye movement has become a new behavioral feature for biometric authentication. In the eye movement-based authentication methods that use temporal features and artificial design features, the required duration of eye movement recordings are too long to be
Jinghui Yin, Jiande Sun, Jing Li, Ke Liu
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Deep metric learning to rank [PDF]
, 2019 We propose a novel deep metric learning method by revisiting the learning to rank approach. Our method, named FastAP, optimizes the rank-based Average Precision measure, using an approximation derived from distance quantization.
Cakir, Fatih +4 more
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