Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces. At the ...

Kernel methods are a class of machine learning algorithms which learn and discover patterns in a high (possibly infinite) dimensional feature space obtained by often nonlinear, possibly infinite ...

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 ...