KNN "no learning involved": one probaby wants to cross-validate K at the least, if not learn the metric.
Some methods say online learning isn't applicable. As pointed out elsewhere, objectives for K-means and mixture models could be fitted with stochastic gradient descent. In general there is always an online option. For example, keep a restricted set of items and chuck out ones that seem less useful as others come in.
Good point about using cross-validation to learn K, I forgot about that. I added this to the cheat sheet.
Also regarding online learning methods, I was probably a bit quick to dismiss certain algorithms as not supporting online learning; in coursework we unfortunately didn't have time to delve into all aspects of all algorithms. I've rewritten the Online column as "To be added." for those online methods I'm not familiar with (yet). Someone else is, of course, free to fork it on Github: http://github.com/Emore/mlcheatsheet
Comments
KNN "no learning involved": one probaby wants to cross-validate K at the least, if not learn the metric.
Some methods say online learning isn't applicable. As pointed out elsewhere, objectives for K-means and mixture models could be fitted with stochastic gradient descent. In general there is always an online option. For example, keep a restricted set of items and chuck out ones that seem less useful as others come in.
(Aside: I have a very introductory lecture to machine learning on the web: http://videolectures.net/bootcamp2010_murray_iml/ — not for anyone that knows the methods on this cheat sheat!)
Thanks for the comments!
Good point about using cross-validation to learn K, I forgot about that. I added this to the cheat sheet.
Also regarding online learning methods, I was probably a bit quick to dismiss certain algorithms as not supporting online learning; in coursework we unfortunately didn't have time to delve into all aspects of all algorithms. I've rewritten the Online column as "To be added." for those online methods I'm not familiar with (yet). Someone else is, of course, free to fork it on Github: http://github.com/Emore/mlcheatsheet