book

Visual Population Codes -- edited by Nikolaus Kriegeskorte and Gabriel Kreiman

Chapter 18. Introduction to Statistical Learning and Pattern Classification (Jed Singer and Gabriel Kreiman)

We provide a non-exhaustive list of links that may help the user interested in implementing and/or using some of the ideas in this chapter.

If you have relevant material that should be added to this list, or to report broken links, please email gabriel.kreiman at tch.harvard.edu

 

www.nr.com
Numerical Recipes (The Art of Scientific Computing)
cbcl.mit.edu/software-datasets/index.html
Center for Biological and Computational Learning at MIT
http://www.support-vector-machines.org/
Literature and links to SVM software
http://www.mathworks.com/matlabcentral/fileexchange/
Mathwork's file exchange
http://sccn.ucsd.edu/eeglab/
EEGLAB: MATLAB toolbox for ICA and other analyses on multichannel data
http://afni.nimh.nih.gov/pub../pub/dist/doc/program_help/3dsvm.html
AFNI 3dsvm
http://code.google.com/p/princeton-mvpa-toolbox/
Princeton MVPA toolbox
http://www.pymvpa.org
Python MVPA toolbox
http://www.csie.ntu.edu.tw/~cjlin/libsvm
LIBSVM toolbox

http://www.ibtb.org/

http://code.google.com/p/pyentropy/

Stefano Panzeri's toolbox
http://www.readout.info
Ethan Meyers' toolboxes
 
Fabrizio Gabbiani's spike train analysis techniques
http://www.cs.cornell.edu/People/tj/svm_light/svm_hmm.html SVMhmm
Top