Human algorithmic stability and human Rademacher complexity
Mehrnoosh Vahdat, Luca Oneto, Alessandro Ghio, Davide Anguita, Mathias Funk, Matthias Rauterberg · TU/e Research Portal · 2015
Abstract. In Machine Learning (ML), the learning process of an algo-rithm given a set of evidences is studied via complexity measures. The way towards using ML complexity measures in the Human Learning (HL) domain has been paved by a previous study, which introduced Human Rademacher Complexity (HRC): in this work, we introduce Human Algo-rithmic Stability (HAS). Exploratory experiments, performed on a group of students, show the superiority of HAS against HRC, since HAS allows grasping the nature and complexity of the task to learn. 1