<commit_before>Kernel Methods Toolbox
======================

A MATLAB toolbox for nonlinear signal processing and machine learning

Author: <NAME>

Official web: https://sourceforge.net/projects/kmbox  


About
---

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", by <NAME>, defended in 2010.

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


Copyright notice
---

The code has been developed and copyrighted © 2014 by <NAME>. It is distributed under the terms of the BSD (3-Clause) License. In short, this means that everyone is free to use it, to modify it and to redistribute it on a free basis. It is not in the public domain; it is copyrighted and there are restrictions on its distribution (see LICENSE.txt).


Installation
---

Extract the zip file. Add the toolbox' main folder and subfolders to the 
MATLAB path.


Usage
---

The name of each function uses the prefix "km_" to minimize interference with other toolboxes. Usage of each function is specified in the function file itself.

Most algorithms have a corresponding demonstration file in the "demo" folder that bears the suffix "_demo". These programs can be executed without setting any additional parameters.

The code uses the following conventions:
- For data matrices, data is stored and accessed in row format: each data point is a row in the data matrix.


Citing KMBOX
---

If you use this toolbox in your research please cite
@phdthesis {vanvaerenbergh2010kernel,
	author = {<NAME>, Steven}
	title = {Kernel methods for nonlinear identification, equalization and separation of signals},
	year = {2010},
	school = {University of Cantabria},
	month = feb,
	note = {Software available at \url{http://sourceforge.net/projects/kmbox/}}
}


Contact<commit_msg>install instructions<commit_after>Kernel Methods Toolbox
======================

A MATLAB toolbox for nonlinear signal processing and machine learning

Author: <NAME>

Official web: https://sourceforge.net/projects/kmbox  


About
---

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", by <NAME>, defended in 2010.

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


Copyright notice
---

The code has been developed and copyrighted © 2014 by <NAME>. It is distributed under the terms of the BSD (3-Clause) License. In short, this means that everyone is free to use it, to modify it and to redistribute it on a free basis. It is not in the public domain; it is copyrighted and there are restrictions on its distribution (see LICENSE.txt).


Installation
---

1. Run `install.m` to add the library folder to the path.
2. Type `savepath` to save the changes to the path.

Usage
---

The name of each function uses the prefix "km_" to minimize interference with other toolboxes. Usage of each function is specified in the function file itself.

Most algorithms have a corresponding demonstration file in the "demo" folder that bears the suffix "_demo". These programs can be executed without setting any additional parameters.

The code uses the following conventions:
- For data matrices, data is stored and accessed in row format: each data point is a row in the data matrix.


Citing KMBOX
---

If you use this toolbox in your research please cite
@phdthesis {vanvaerenbergh2010kernel,
	author = {<NAME>, Steven}
	title = {Kernel methods for nonlinear identification, equalization and separation of signals},
	year = {2010},
	school = {University of Cantabria},
	month = feb,
	note = {Software available at \url{http://sourceforge.net/projects/kmbox/}}
}


Contact
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