All the algorithms requiring training can be optimized using stochastic gradient descent-- which is very effective for large data sets (see http://leon.bottou.org/research/stochastic)
Also, here are some additions for the online learning column:
The course unfortunately couldn't cover all material on all algorithms, so the cheat sheet basically reflects my own knowledge rather than what's possible. I've referenced the Online SVM and Online Mixture model though, thanks for those.
Also, I'll have to look into stochastic gradient descent!
Also, here are some additions for the online learning column:
* Online SVM: http://www.springerlink.com/index/Y8666K76P6R5L467.pdf
* Online gaussian mixture estimation: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.87....
One more thing: why no random forests? Or decision tree ensembles of any sort?