Name
Abstract | ||
|---|---|---|
Artificial intelligence systems are frequently used to solve various problems in our daily lives. However, these systems require problem-specific big data to facilitate their learning processes. Unfortunately, for unknown environments, there are no previous instances available for learning. To support such learning in unknown environments, we propose a novel hybrid learning system that facilitates collaborative learning between humans and artificial intelligence systems. In this study, we verified that the proposed system accelerated the both human and machine learning by employing a simplified color design task. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SOCPAR.2015.7492774 | 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) |
Keywords | Field | DocType |
collaborative learning,accelerated learning,human skill,general regression neural network | Robot learning,Competitive learning,Instance-based learning,Active learning (machine learning),Computer science,Hyper-heuristic,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning,Learning classifier system | Conference |
ISSN | Citations | PageRank |
2381-7542 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ogiso, T. | 1 | 4 | 1.20 |
| Koichiro Yamauchi | 2 | 220 | 21.09 |
| Norio Ishii | 3 | 0 | 0.34 |
| Yuri Suzuki | 4 | 1 | 2.73 |