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
| http://numerical.recipes | Numerical Recipes (The Art of Scientific Computing) |
| http://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://klab.tch.harvard.edu/code/code.html | Kreiman lab code repository |
| 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 |
Stefano Panzeri's toolbox |
|
| http://www.readout.info | Ethan Meyers' toolboxes |
| https://www.caam.rice.edu/~cox/booksite/ | Fabrizio Gabbiani's spike train analysis techniques |
| http://www.cs.cornell.edu/People/tj/svm_light/svm_hmm.html | SVMhmm |