AIMC Topic: Bayes Theorem

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Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise.

Neural networks : the official journal of the International Neural Network Society
Feedforward neural networks (FFNN) are among the most used neural networks for modeling of various nonlinear problems in engineering. In sequential and especially real time processing all neural networks models fail when faced with outliers. Outliers...

Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Journal of biomedical informatics
OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.

Effect of reporting bias in the analysis of spontaneous reporting data.

Pharmaceutical statistics
It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of associa...

Machine-learning approaches in drug discovery: methods and applications.

Drug discovery today
During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and ...

Automated tracking and analysis of behavior in restrained insects.

Journal of neuroscience methods
BACKGROUND: Insect behavior is often monitored by human observers and measured in the form of binary responses. This procedure is time costly and does not allow a fine graded measurement of behavioral performance in individual animals. To overcome th...

Prediction of hospitalization due to heart diseases by supervised learning methods.

International journal of medical informatics
BACKGROUND: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that n...

Deep learning of support vector machines with class probability output networks.

Neural networks : the official journal of the International Neural Network Society
Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically i...

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

Health care management science
A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital'...

Aggregate features in multisample classification problems.

IEEE journal of biomedical and health informatics
This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditiona...

Shortcomings of deep learning for distributional predictors: a note.

Biostatistics (Oxford, England)
A number of domains in biomedical research use data with a large number of predictors all representing the same type of measurement. Often, an important summary is the within-person distribution of these predictors. Here we focus on settings where th...