Buscar
Mostrando ítems 1-10 de 310
A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm
Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution ...
A bayesian networks structure learning: A scoring and search based approachAprendizaje estructural de redes bayesianas: Un enfoque basado en puntaje y búsqueda
(Universidad Militar Nueva Granada, 2011)
A bayesian networks structure learning: A scoring and search based approachAprendizaje estructural de redes bayesianas: Un enfoque basado en puntaje y búsqueda
(Universidad Militar Nueva Granada, 2011)
Factors associated with medical students' scores on the National Licensing Exam in Peru: a systematic review
(Korea Health Personnel Licensing Examination Institute, 2022)
PURPOSE: This study aimed to identify factors that have been studied for their associations with National Licensing Examination (ENAM) scores in Peru. METHODS: A search was conducted of literature databases and registers, ...
Deepsigns: a predictive model based on deep learning for the early detection of patient health deterioration
(Universidade do Vale do Rio dos Sinos, 2020-09-29)
CONTEXT: The accurate and early diagnosis of critically ill patients depends on medical staff’s attention and the observation of different variables, vital signs, and laboratory test results, among others. Seriously ill ...
Deepsigns: a predictive model based on deep learning for the early detection of patient health deterioration
(Universidade do Vale do Rio dos Sinos, 2020-09-29)
CONTEXT: The accurate and early diagnosis of critically ill patients depends on medical staff’s attention and the observation of different variables, vital signs, and laboratory test results, among others. Seriously ill ...
GeoGebra as learning tool for the search of the roots of functions in numerical methods
(IOP Publishing, 2020)
Physics is capable of describing, through equations, phenomena on a micro and macroscopic scale. However, most of these equations are non-linear and the identification of their roots requires the use of approximation ...
Maximum entropy-based reinforcement learning using a confidence measure in speech recognition for telephone speech
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010)
In this paper, a novel confidence-based reinforcement learning (RL) scheme to correct observation log-likelihoods and to address the problem of unsupervised compensation with limited estimation data is proposed. A two-step ...
TERL: classification of transposable elements by convolutional neural networks
(2021-05-20)
Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide ...