Eamonn O'Brien-Strain's Library tagged → View Popular, Search in Google
For sorting user ratings the best algorithm is lower bound of Wilson score confidence interval for a Bernoulli parameter (via +Christopher Hoover)
Nice survey of the various social-network influence players, including some interesting math on how one of them calculates "influence" in terms of probabilities that your postings have been read by others. (Note however, that this article was posted more than a year ago, which is a old in Internet-time.)
How Facebook filters items to only show you what it thinks you would be most interested in seeing.
A very flexible Java programming framework that integrates many different Java matrix libraries.
A comprehensive Java matrix library, including support for iterative solvers and native speed-up for dense matrices.
More recent version of the "optimal" code, including pointers to where it is included in the Mines Java Toolkit.
Java package for solving systems of linear equations
Comparing photos with tagged Flickr photos to determine their geographic location.
A new clustering algorithm that claims to be much faster than existing clustering algorithms while still producing very good results. Unlike K-Means it does not require the number of clusters to be given a priori.
Base64 encoder which claims to be much faster than the iharder encoder that we currently use. Might consider using it, but it would need a little tweaking to provide an alternative "URL-safe" alphabet mode like the iharder library does.
Popular algorithm for characterizing an image as a 128-dimensional vector.
This is the particular form of the Hermite spline that I have been using for my trajectories, though I have just been dealing with two points at a time.
In the code that I have been writing for the last year or two I specify a trajectory by giving the values and time derivitives of two control points. Thanks to my co-worker Ramin for pointing out that this technique is called a Hermite spline.
Includes useful step-by-step instructions for doing principal component analysis for reducing the dimensionality of a dataset.
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