Kernel methods and support vector machines (SVMs) serve as cornerstones in modern machine learning, offering robust techniques for both classification and regression tasks. At their core, kernel ...

Kernel methods have emerged as a powerful tool in adaptive filtering and system identification, enabling the processing and modelling of complex, nonlinear relationships in dynamic systems. By mapping ...

This repository contains the handing for the MVA master's class: Kernel methods for machine learning: http://members.cbio.mines-paristech.fr/~jvert/svn/kernelcourse ...

In machine learning, traditional linear prediction techniques are well understood and methods for their efficient solution have been developed. Many real-world applications are better modelled using ...

Strkernel is a python package designed to perform a kernel based analysis of biological sequences. The implementation assumes the use of Support Vector Machines (SVMS) but does not strictly require it ...

Abstract: Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expertise and ...

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled ...