Abstract: In their fundamental paper published in 1965, G. Solomon and J. J. Stiffler invented infinite families of codes meeting the Griesmer bound. These codes are then called Solomon-Stiffler codes ...
Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
Want to understand how multivariate linear regression really works under the hood? In this video, we build it from scratch in C++—no machine learning libraries, just raw code and linear algebra. Ideal ...
ABSTRACT: There is a set of points in the plane whose elements correspond to the observations that are used to generate a simple least-squares regression line. Each value of the independent variable ...
Although [Vitor Fróis] is explaining linear regression because it relates to machine learning, the post and, indeed, the topic have wide applications in many things that we do with electronics and ...
% Ref. [1] S. Chapra, "Applied numerical methods with MATLAB", Mc Craw Hill, Singapore (2008). % The linear function of x1 and x2 to be fitted: y(x) = a0 + a1*x1 + a2 ...
% This Matlab code performs a calculation of polynomial regression [1]. % Ref. [1] S. Chapra, "Applied numerical methods with MATLAB", Mc Craw Hill, Singapore (2008). % The second-order polynomial or ...
Abstract: This article introduces a novel concatenated coding scheme called sparse regression LDPC (SR-LDPC) codes. An SR-LDPC code consists of an outer non-binary LDPC code and an inner sparse ...
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