We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate...
Alina Beygelzimer, Daniel Hsu, John Langford, Tong...
Abstract New application areas or new technical innovations expect from database management systems more and more new functionality. However, adding functions to the DBMS as an int...
The aim of this paper is to study an Information Theory based learning theory for neural units endowed with adaptive activation functions. The learning theory has the target to fo...
We propose a new algorithm for the estimation of functional activations in diffuse optical imaging. Our approach considers the activations to be support limited. We simultaneously...
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...