Results 271 to 280 of about 210,501 (299)

Metric Learning for Image Alignment

International Journal of Computer Vision, 2009
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Minh Hoai Nguyen, Fernando De la Torre
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Metric learning for reinforcement learning agents

International Joint Conference on Autonomous Agents and Multiagent Systems, 2011
A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-coded state representations, there has been a growing interest in learning this representation.
Matthew E. Taylor, Brian Kulis, Fei Sha
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Learning and growth strategy metrics

Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing - CompSysTech '08, 2008
Purpose -- to examine the best practices and metrics of a balanced scorecard BSC learning and growth view for academic education and research institution and to present process, targets and metrics. Design/methodology/approach -- based on an extensive literature review and analysis and on the authors exercise in information systems, education and ...
Petko Ruskov, Yanka Todorova
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Deep Meta Metric Learning

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
In this paper, we present a deep meta metric learning (DMML) approach for visual recognition. Unlike most existing deep metric learning methods formulating the learning process by an overall objective, our DMML formulates the metric learning in a meta way, and proves that softmax and triplet loss are consistent in the meta space.
Guangyi Chen 0002, Tianren Zhang, Jiwen Lu, Jie Zhou 0001   +3 more
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Metric learning for text documents

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
Many algorithms in machine learning rely on being given a good distance metric over the input space. Rather than using a default metric such as the Euclidean metric, it is desirable to obtain a metric based on the provided data. We consider the problem of learning a Riemannian metric associated with a given differentiable manifold and a set of points ...
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Metric learning for image steganalysis

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015
Image steganalysis based on supervised distance metric learning is to find an appropriate measure of similarity between image features where the distribution discrepancy between cover-images and stego-images are analyzed in the reduced dimensional space.
Guoming Chen 0001, Qiang Chen, Dong Zhang 0002   +2 more
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Deep Variational Metric Learning

2018
Deep metric learning has been extensively explored recently, which trains a deep neural network to produce discriminative embedding features. Most existing methods usually enforce the model to be indiscriminating to intra-class variance, which makes the model over-fitting to the training set to minimize loss functions on these specific changes and ...
Xudong Lin 0003, Yueqi Duan, Qiyuan Dong, Jiwen Lu, Jie Zhou 0001   +4 more
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Broad Metric Learning: A Fast and Efficient Discriminative Metric Learning Model

IEEE Transactions on Cybernetics
Metric learning aims to learn a discriminative metric space, where samples of the same class stay close, and those of different classes far apart. Existing classical metric learning methods based on linear transformation have limited learning performance due to the low representation capability.
Xiaoman Hu, C. L. Philip Chen, Tong Zhang 0015   +2 more
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Multi-metric learning by a pair of twin-metric learning framework

Applied Intelligence, 2022
Min Zhang, Liming Yang, Chao Yuan, Qiangqiang Ren   +3 more
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Multiple metric learning via local metric fusion

Information Sciences, 2023
Chuangyin Dang, Jiye Liang, Long Wei
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