Results 221 to 230 of about 314,651 (266)
Machine learning in mental health promotion for older adults: a scoping review. [PDF]
BMC GeriatrRuan Y, Liang H, Yamamoto S, Lin S.
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Diagnostic Performance and Reliability of RADS Classification Systems for Solitary Bone Lesions. [PDF]
Radiol Imaging CancerHarlianto NI.
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Inclusion and exclusion in gaming communities: a systematic review and future directions to increase inclusivity. [PDF]
Front PsycholArgaman Y, Gur T, Heller B, Maaravi Y.
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Identification of monotonically classifying pairs of genes for ordinal disease outcomes. [PDF]
Bioinform AdvFourquet O +6 more
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A Novel Digital Phenotype for Burn Sepsis: Leveraging Electronic Health Record Data and Natural Language Processing to Improve Case Definition. [PDF]
Appl Clin InformSoulakis ND +3 more
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ACM Computing Surveys, 2016
In online scenarios requests arrive over time, and each request must be serviced in an irrevocable manner before the next request arrives. Online algorithms with advice is an area of research where one attempts to measure how much knowledge of future requests is necessary to achieve a given performance level, as defined by the competitive ratio.
Joan Boyar +4 more
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In online scenarios requests arrive over time, and each request must be serviced in an irrevocable manner before the next request arrives. Online algorithms with advice is an area of research where one attempts to measure how much knowledge of future requests is necessary to achieve a given performance level, as defined by the competitive ratio.
Joan Boyar +4 more
openaire +2 more sources
An online clustering algorithm
2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation ...
Kan Li, Fenglan Yao, Ruipeng Liu
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Online Pairwise Learning Algorithms
Neural Computation, 2016Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing
Yiming Ying, Ding-Xuan Zhou
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