Circulation. Cardiovascular quality and outcomes
Nov 8, 2016
BACKGROUND: Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly ...
Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A no...
Screening tests are used in forensic science for field testing and directing laboratory analysis of physical evidence. These tests are often binary in that the data produced is interpreted as yes/no or present/absent. The utility of screening assays ...
Computational and mathematical methods in medicine
Nov 17, 2015
Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean cons...
Neural networks : the official journal of the International Neural Network Society
Oct 19, 2015
Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generali...
Computational intelligence and neuroscience
May 21, 2015
Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonpara...
Physical review. E, Statistical, nonlinear, and soft matter physics
May 18, 2015
Learning in restricted Boltzmann machine is typically hard due to the computation of gradients of log-likelihood function. To describe the network state statistics of the restricted Boltzmann machine, we develop an advanced mean-field theory based on...
Neural networks : the official journal of the International Neural Network Society
May 9, 2015
Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation proce...
Neural networks : the official journal of the International Neural Network Society
May 8, 2015
In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent v...
Expert opinion plays an important role when choosing clusters of chemical compounds for further investigation. Often, the process by which the clusters are assigned to the experts for evaluation, the so-called selection process, and the qualitative r...
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