Abstract: In this paper we present a novel approach for nonlinear time series prediction using kernel methods. The kernel methods such as support vector machine (SVM) and support vector regression ...

Abstract: Feature selection accelerates training, enhances interpretability, and reduces data dimensionality by identifying the most relevant and important features, thereby improving machine learning ...

Code for "Robust Nonlinear System Identification using Reproducing Kernel Hilbert Spaces" This repository contains the source code and supplementary material for the paper: Jannis O. Lübsen and Annika ...

This repository contains a comprehensive tutorial on Support Vector Machines (SVMs), with a focus on how different kernel functions affect classification performance. The tutorial uses a synthetic 2D ...

In many physics and engineering applications, data-generating processes are characterized by intrinsic geometric structures, such as symmetries, conservation laws, variational principles, or ...