ABSTRACT: Variable selection using penalized estimation methods in quantile regression models is an important step in screening for relevant covariates. In this paper, we present a one-step estimation ...
Abstract: We propose a variational Bayesian (VB) implementation of block-sparse Bayesian learning (BSBL) to compute proxy probability density functions (PDFs) that approximate the posterior PDFs of ...
Abstract: Space-time adaptive processing (STAP) is a key technique for suppressing clutter. We develop a unified correlated sparse Bayesian learning (CSBL) framework to improve clutter suppression in ...
Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...
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Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian ...
Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical ...
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