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Group project for HKU COMP3314_CSIS0314(Spring 2015), attacking a real world machine learning problem of classifying tiny images from a subset of CIFAR-10. Our result ranks top 1 among all groups.

For more details, please click on the Kaggle page or refer to Specification.pdf.

More on our methods and results, please refer to Final Report.

Find us on the Leader Board. Cheers!

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

minFunc, written by Mark Schmidt, whose 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."

See: http://www.cs.stanford.edu/~acoates/

kmeans demo, provided by Adam Coates on paper Analysis of Single Layer Unsupervised Feature Learning.