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: Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of ...
This blog post and audio file is another in the series "Defending the Algorithm™" written, edited and narrated by Pittsburgh, Pennsylvania Business, IP and AI Trial Lawyer Henry M. Sneath, Esq. and ...
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 ...
This blog post and audio file is another in the series "Defending the Algorithm™" written and edited by Pittsburgh, Pennsylvania Business, IP and AI Trial Lawyer Henry M. Sneath, Esq. and was authored ...
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 ...
When it comes to teaching math, a debate has persisted for decades: How, and to what degree, should algorithms be a focus of learning math? The step-by-step procedures are among the most debated ...
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