Results 61 to 70 of about 187,533 (264)
Metric Selection and Metric Learning for Matching Tasks [PDF]
, 2020 A quarter of a century after the world-wide web was born, we have grown accustomed to having easy access to a wealth of data sets and open-source software. The value of these resources is restricted if they are not properly integrated and maintained. A lot of this work boils down to matching; finding existing records about entities and enriching them ...
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Person‐centred follow‐up care based on evidence‐based clinical practice guidelines and providing individualised information should help to inform and reassure survivors about their medical and psychosocial situation and provide treatment and support where needed.
Gisela Michel +36 more
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Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering
IEEE Access, 2022 Standard supervised classification methods make the assumption that the training data is fully annotated thus requiring an a-priory labelling process which is both costly and time-consuming.
Amedeo Racanati +2 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Japan's 2024 physician work‐style reform introduced legally binding limits on physicians’ working hours, but its impact on education and workforce sustainability in pediatric hematology–oncology (PHO) remains unclear. Procedure We conducted a repeated cross‐sectional study with structured quantitative items and free‐text responses ...
Kiyohiko Kaizu +7 more
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Frontiers in Neuroscience, 2020 Metric learning is a class of efficient algorithms for EEG signal classification problem. Usually, metric learning method deals with EEG signals in the single view space. To exploit the diversity and complementariness of different feature representations,
Jing Xue, Xiaoqing Gu, Tongguang Ni
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
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IEEE Access, 2019 This article presents a concrete mathematical analysis on Information-Theoretic Metric Learning (ITML). The analysis provides a theoretical foundation for ITML, by supplying well-posedness, strong duality, and convergence.
Jooyeon Choi, Chohong Min, Byungjoon Lee
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CoRR, 2012 We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relative volume of a given set of points. We derive the details for a family of metrics on the multinomial simplex.
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Coordinated Local Metric Learning [PDF]
2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015 Mahalanobis metric learning amounts to learning a linear data projection, after which the L2 metric is used to compute distances. To allow more flexible metrics, not restricted to linear projections, local metric learning techniques have been developed.
Saxena, Shreyas, Verbeek, Jakob
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Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
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