Results 141 to 150 of about 254,803 (193)
Prescription Digital Therapeutics Research Across Clinical, Engagement, Regulatory, and Implementation Domains: A Bibliometric and Thematic Study. [PDF]
CureusLakhan SE.
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Global trends in e-labeling: a comprehensive geographical perspective. [PDF]
Front Med (Lausanne)Dangy-Caye A +6 more
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Mapping 60 Years of Discovery: An AI-Driven Bibliometric and Altmetric Analysis of the Journal of Periodontal Research. [PDF]
J Periodontal ResHazrati P +4 more
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Coming to Terms with the Alcohol-Cancer Link: A Work in Progress. [PDF]
Cancer Epidemiol Biomarkers PrevKlein WMP, Jesch E.
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2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009
An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy.
STELLA, FABIO ANTONIO +3 more
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An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy.
STELLA, FABIO ANTONIO +3 more
openaire +1 more source
2013 2nd IAPR Asian Conference on Pattern Recognition, 2013
We propose a Label-Related/Unrelated Topic Switching Model (LRU-TSM) based on Latent Dirichlet Allocation (LDA) for modeling a labeled corpus. In this model, each word is allocated to a label-related topic or a label-unrelated topic. Label-related topics utilize label information, and label-unrelated topics utilize the framework of Bayesian ...
Yasutoshi Ida +2 more
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We propose a Label-Related/Unrelated Topic Switching Model (LRU-TSM) based on Latent Dirichlet Allocation (LDA) for modeling a labeled corpus. In this model, each word is allocated to a label-related topic or a label-unrelated topic. Label-related topics utilize label information, and label-unrelated topics utilize the framework of Bayesian ...
Yasutoshi Ida +2 more
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International Journal of Database Theory and Application, 2015
Most of the models not aware of these dependencies on document time stamps. Not modeling time can confound co-occurrence patters and results in exchangeability of topic problem, which is important factor to deal with when finding dynamic topic discovery.
Yong Heng Chen +3 more
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Most of the models not aware of these dependencies on document time stamps. Not modeling time can confound co-occurrence patters and results in exchangeability of topic problem, which is important factor to deal with when finding dynamic topic discovery.
Yong Heng Chen +3 more
openaire +1 more source