The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict a person's bank savings account balance based on their age, years of ...

Standard techniques for selecting the bandwidth of a kernel estimator from the data in a nonparametric regression model perform badly when the errors are correlated. In this paper we propose a ...

This paper is focused on kernel regression when the response variable is the shape of a 3D object represented by a configuration matrix of landmarks. Regression methods on this shape space are not ...

Testing is an integral and important part of any software development cycle, open or closed, and Linux kernel is no exception to that. Developer testing, integration testing, regression, and stress ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...