AIMC Topic: Regression Analysis

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Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

Accident; analysis and prevention
In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correl...

Revisiting Warfarin Dosing Using Machine Learning Techniques.

Computational and mathematical methods in medicine
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographi...

Image Quality Assessment Using Human Visual DOG Model Fused With Random Forest.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Objective image quality assessment (IQA) plays an important role in the development of multimedia applications. Prediction of IQA metric should be consistent with human perception. The release of the newest IQA database (TID2013) challenges most of t...

Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.

Brain structure & function
Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neu...

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society
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...

Asymptotic accuracy of Bayesian estimation for a single latent variable.

Neural networks : the official journal of the International Neural Network Society
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...

Applying a novel combination of techniques to develop a predictive model for diabetes complications.

PloS one
Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according ...

Noninvasive reconstruction of cardiac transmembrane potentials using a kernelized extreme learning method.

Physics in medicine and biology
Non-invasively reconstructing the cardiac transmembrane potentials (TMPs) from body surface potentials can act as a regression problem. The support vector regression (SVR) method is often used to solve the regression problem, however the computationa...

A pressure control method for emulsion pump station based on Elman neural network.

Computational intelligence and neuroscience
In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techn...