We consider learning in situations where the function used to classify examples may switch back and forth between a small number of different concepts during the course of learnin...
In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
This article proposes an algorithm to automatically learn useful transformations of data to improve accuracy in supervised classification tasks. These transformations take the for...
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Rd from random samples. The method is based on the convergence rates of a certain U-statisti...
The central question in this paper is: Who (or what) constructs anticipations? I challenge the (tacit) assumption of Rosen’s standard definition of anticipatory systems according...