The upcoming Volume 9 of the Journal of Machine Learning Research is dedicated a chunk of its pages to a paper entitled “Evidence Contrary to the Statistical View of Boosting” by David Mease and Abraham Wyner. Following this is a number of responses by heavyweights including boosting’s earliest proponents, Freund and Schapire, as well as [...]
Just when I thought I was starting to get my head around the multitudinous uses of convexity in statistics I was thrown by the following definition:
A function f over the interval (a,b) is convex if, for all choices of {x,y,z} satisfying a < x < y < z < b the [...]
I’ve been attempting to read an interesting NIPS 2007 paper entitled Estimating divergence functionals and the likelihood ratio by convex risk minimzation and realised my knowledge of convex analysis was sketchy at best.
Fortunately, Wikipedia pointed me to an excellent summary of the Legendre-Fenchel transformation by Hugo Touchette. A bit more digging around Hugo’s site led [...]
Things have been a little quite around here of late, mainly because I’ve been working on a submission for the NIPS 2007 Workshop on Principles of Learning Problem Design in early December.
I’m pleased to say that I’ll be presenting some recent results that Bob and I have been working on under the heading of “Representations [...]
Last week I saw an interesting PhD monitoring presentation by Justin Bedo on the counter-intuitive phenomenon of “anti-learning”. For certain datasets, learning a classifier from a small number of samples and inverting its predictions performs much better than the original classifier. Most of the theoretical results Justin mentioned about are discussed in a recent paper and [...]