AIMC Topic: Bayes Theorem

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Statistical foundation of Variational Bayes neural networks.

Neural networks : the official journal of the International Neural Network Society
Despite the popularism of Bayesian neural networks (BNNs) in recent years, its use is somewhat limited in complex and big data situations due to the computational cost associated with full posterior evaluations. Variational Bayes (VB) provides a usef...

The exact asymptotic form of Bayesian generalization error in latent Dirichlet allocation.

Neural networks : the official journal of the International Neural Network Society
Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clari...

Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation.

Journal of environmental management
Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Sh...

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data.

Medical & biological engineering & computing
In the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the profusion of genes number, the performance of classifying DNA microarray gene expression data using st...

Machine learning algorithms to predict seizure due to acute tramadol poisoning.

Human & experimental toxicology
INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making.

OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction.

PloS one
Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protei...

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

Journal of psychiatric research
Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from uns...

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy.

Medical physics
PURPOSE: New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real-time ultrasound (US) images. This is ne...

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network.

Magnetic resonance in medicine
PURPOSE: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squ...