K-means feature learning on CIFAR
----------------------------------

To run the demo, open kmeans_demo.m and modify the CIFAR_DIR variable
to point to the directory containing your CIFAR training and test
data.  (These are the MATLAB files, which you can download from
http://www.cs.toronto.edu/~kriz/cifar-10-matlab.tar.gz).

Run matlab, change into the kmeans_demo directory and run
"kmeans_demo".  The script will print out the error on the test set
when finished.  Due to randomness in the K-means initialization,
results may vary, but are generally > 77%.  No cross-validation is
done by this script -- the SVM regularization parameter is copied from
the results obtained by our own validation on a random 80%/20% split
of the training data.


minFunc optimization package
----------------------------------
See:  http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html

minFunc, written by Mark Schmidt, is included with this code.  The
minFunc license follows:

"This software is made available under the Creative Commons 
Attribution-Noncommercial License.  You are free to use, copy, modify, and 
re-distribute the work.  However, you must attribute any re-distribution or 
adaptation in the manner specified below, and you may not use this work for 
commercial purposes without the permission of the author.

Any re-distribution or adaptation of this work must contain the author's name 
and a link to the software's original webpage.  For example, any 
re-distribution of the 'minFunc' software must contain a link to:
http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html

This software comes with no guarantees, and all use of these codes is
entirely at the user's own risk."
