Kernel Methods Toolbox

Version 1.2.0.0 (46.7 KB) by Steven Van Vaerenbergh
A MATLAB toolbox for nonlinear signal processing and machine learning
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Updated 19 Jul 2016
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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.

Cite As

Steven Van Vaerenbergh (2024). Kernel Methods Toolbox (https://github.com/steven2358/kmbox), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2009b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.2.0.0

update description
1.0.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.