Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | ESANN | Information system,Stability (learning theory),Computer science,Rademacher complexity,Human learning,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehrnoosh Vahdat | 1 | 9 | 2.27 |
| Luca Oneto | 2 | 830 | 63.22 |
| Alessandro Ghio | 3 | 667 | 35.71 |
| Davide Anguita | 4 | 1001 | 70.58 |
| Mathias Funk | 5 | 112 | 29.69 |
| Matthias Rauterberg | 6 | 1212 | 209.22 |