Results 221 to 230 of about 437,723 (274)

Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications. [PDF]

Bioengineering (Basel)
Liang J, Zhou Y, Ma K, Jia Y, Zhang Y, Han B, Xiang M.   +6 more
europepmc   +1 more source

Structured Expert Elicitation of Dependence Between River Tributaries Using Nonparametric Bayesian Networks. [PDF]

Risk Anal
Rongen G, Morales-Nápoles O, Worm D, Kok M.   +3 more
europepmc   +1 more source

Named Entity Recognition for Chinese Cancer Electronic Health Records-Development and Evaluation of a Domain-Specific BERT Model: Quantitative Study.

JMIR Med Inform
Chen J, Zhao B, Tian X, Zou Z, Wang R, Wu J, Du S, Guo F.   +7 more
europepmc   +1 more source

Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.

JMIR Med Inform
Dahl S, Bøgsted M, Sagi T, Vesteghem C.
europepmc   +1 more source

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Hidden Conditional Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.
Ariadna, Quattoni, Sybor, Wang, Louis-Philippe, Morency, Michael, Collins, Trevor, Darrell   +4 more
openaire   +2 more sources

Efficient robust conditional random fields

IEEE Transactions on Image Processing, 2015
Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features ...
Dongjin, Song, Wei, Liu, Tianyi, Zhou, Dacheng, Tao, David A, Meyer   +4 more
openaire   +2 more sources

Kernel conditional random fields

Twenty-first international conference on Machine learning - ICML '04, 2004
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs.
John Lafferty, Xiaojin Zhu, Yan Liu
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Conditioned Simulations of Random Velocity Fields

Mathematical Geosciences, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Geraets, David, Lajaunie, Christian, Ruffo, P.   +2 more
openaire   +1 more source