Published 2015
| Version v1
Publication
Human algorithmic stability and Human Rademacher Complexity
Description
In Machine Learning (ML), the learning process of an algorithm 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 Algorithmic 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.
Additional details
Identifiers
- URL
- http://hdl.handle.net/11567/845888
- URN
- urn:oai:iris.unige.it:11567/845888
Origin repository
- Origin repository
- UNIGE