Results 71 to 80 of about 187,533 (264)
Metric Learning-Based Multi-Instance Multi-Label Classification With Label Correlation
IEEE Access, 2019 In multi-instance multi-label learning (MIML) problems, predicting the labels of unseen bags becomes difficult when the labels of their instances are not provided directly.
Haifeng Hu +3 more
doaj +1 more source
Deep transfer metric learning [PDF]
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 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 doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets.
Junlin Hu 0001, Jiwen Lu, Yap-Peng Tan
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The human gut microbiome across the life course
FEBS Letters, EarlyView.Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero +4 more
wiley +1 more source
Learning Distance Metrics for Entity Resolution
IEEE Access, 2018 Entity resolution (ER) is to find database records that refer to the same real-world entity. A key component for ER is to choose a proper distance (similarity) function for each database field to quantify the similarity of records.
Lingli Li +3 more
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Modulation of Homer1 EVH1 domain internal dynamics by putative autism‐associated mutations
FEBS Letters, EarlyView.The putative autism‐associated M65I and S97L variants of the EVH1 domain of the postsynaptic scaffold protein Homer1 do not exhibit substantial changes in their overall structure or partner binding. Both of them, but especially the M65I variant, show altered internal dynamics relative to the wild‐type domain on the μs‐ms timescale, indicated by the ...
Fanni Farkas +6 more
wiley +1 more source
Improved Metric-Based Recommender by Historical Interactions
IEEE Access, 2019 A remarkable success in recommendations has been achieved by using methods based on metric learning, especially in digital marketing. However, the existing methods do not consider the relative preferences among items that users like.
Yubo Jiang, Yunfang Zhu, Xin Du, Tao Jin
doaj +1 more source
Metric Learning to Rank. [PDF]
, 2010 We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by dis- tance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG.
Mcfee, Brian, Lanckriet, Gert
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Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Manifold learning in metric spaces
Applied and Computational Harmonic AnalysisLaplacian-based methods are popular for the dimensionality reduction of data lying in $\mathbb{R}^N$. Several theoretical results for these algorithms depend on the fact that the Euclidean distance locally approximates the geodesic distance on the underlying submanifold which the data are assumed to lie on. However, for some applications, other metrics,
Liane Xu, Amit Singer
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