Online learning algorithms are a class of machine learning (ML) techniques that consume the input as a stream and adapt as they consume input. They are often used for their computational desirability, e.g., for speed, the ability to consume large data sets, and the ability to handle non-convex objectives. However, they come with another useful benefit, namely “sub-linear debugging”.If you are interested in hitting the Doherty threshold in Machine Learning, read the whole thing!
Wednesday, September 24, 2014
I have a post on sub-linear debugging on Microsoft's machine learning blog.