Abstract: Sparse Bayesian learning (SBL)-based methods for wideband direction of arrival (DOA) estimation have shown impressive performance in terms of high-resolution. It generally assumes that all ...
This webinar introduced healthcare researchers to Bayesian meta-analysis methods, challenging the perception that these methods are inaccessible to non-statistical researchers. The session ...
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 ...
Add a description, image, and links to the sparse-bayesian-learning topic page so that developers can more easily learn about it.
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results