Results 31 to 40 of about 201,849 (264)
J. Mach. Learn. Res., 2011 Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in practice when this parameter is ...
Marco Cuturi, David Avis
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Distributed Semi-Supervised Metric Learning
IEEE Access, 2016 Over the last decade, many pairwise-constraint-based metric learning algorithms have been developed to automatically learn application-specific metrics from data under similarity/dissimilarity data-pair constraints (weak labels).
Pengcheng Shen, Xin Du, Chunguang Li
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Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient
Xian Sun +5 more
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Learning similarity metric with SVM [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 In this paper, we show how to learn a good similarity metric for SVM classification. We present a novel approach to simultaneously learn a Mahalanobis similarity metric and an SVM classifier. Different from previous approaches, we optimize the Mahalanobis metric directly for minimizing the SVM classification error.
Xiaoqiang Zhu, +6 more
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Information-theoretic metric learning [PDF]
Proceedings of the 24th international conference on Machine learning, 2007 In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing
Jason V. Davis +4 more
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Learning Compact Metrics for MT [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Accepted at EMNLP ...
Amy Pu +4 more
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Attribute-enhanced metric learning for face retrieval
EURASIP Journal on Image and Video Processing, 2018 Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval.
Yuchun Fang, Qiulong Yuan
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CoRR, 2017 The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider lifelong learning problem to mimic "human learning", i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating previous experiences and knowledge.
Gan Sun +3 more
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Adaptive Multi-Proxy for Remote Sensing Image Retrieval
Remote Sensing, 2022 With the development of remote sensing technology, content-based remote sensing image retrieval has become a research hotspot. Remote sensing image datasets not only contain rich location, semantic and scale information but also have large intra-class ...
Xinyue Li +4 more
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CoRR, 2019 Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean ...
Max Aalto, Nakul Verma
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