Many controlled processes, such as biochemical ones, are repetitive, similar to batch-organized processes. They generate Optimal Control Problems (OCPs) solved by optimal controllers, which often ...
Abstract: This article offers a targeted survey and comparative analysis of regression techniques based on hyperdimensional computing (HDC), and investigates how they compare with traditional ...
Abstract: Nonlinear regression models are essential in various fields such as system identification, data analysis, and signal processing. Despite their importance, the efficiency of robust parameter ...
Decision tree regression is a fundamental machine learning technique to predict a single numeric value. A decision tree regression system incorporates a set of virtual if-then rules to make a ...
A decision tree regression system incorporates a set of if-then rules to predict a single numeric value. Decision tree regression is rarely used by itself because it overfits the training data, and so ...
This C library provides efficient implementations of linear regression algorithms, including support for stochastic gradient descent (SGD) and data normalization techniques. It is designed for easy ...
As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...
Ischemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients ...
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