We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
In this paper we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during the interaction with an intelligent learning environmen...
Abstract — This paper describes a practical technique for the optimal scheduling of control dominated systems minimizing the weighted average latency over all control branches. S...
This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization pro...
We present a probabilistic formulation of UCS (a sUpervised Classifier System). UCS is shown to be a special case of mixture of experts where the experts are learned independentl...
Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, ...