Kernel Methods Toolbox

A MATLAB toolbox for nonlinear signal processing and machine learning

The Kernel Methods Toolbox (KMBOX) is a collection of MATLAB programs that implement kernel-based algorithms, with a focus on regression algorithms and online algorithms. It can be used for nonlinear signal processing and machine learning.
KMBOX includes implementations of algorithms such as kernel principal component analysis (KPCA), kernel canonical correlation analysis (KCCA) and kernel recursive least-squares (KRLS).
The goal of this distribution is to provide easy-to-analyze algorithm implementations, which reveal the inner mechanics of each algorithm and allow for quick modifications. The focus of these implementations is therefore on readability rather than speed or memory usage.
The basis of this toolbox was a set of programs written for the Ph.D. Thesis "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals".

Template files are provided to encourage external authors to include their own code into the toolbox.