MetadataShow full item record
AbstractIn this work we present a novel approach and algorithms for equipping Artificial Intelligence systems with capabilities to become better over time. A distinctive feature of the approach is that, in the supervised setting, the approaches' computational complexity is sub-linear in the number of training samples. This makes it particularly attractive in applications in which the computational power and memory are limited. The approach is based on the concentration of measure effects and stochastic separation theorems. The algorithms are illustrated with examples.
CitationTyukin, I.Y., Gorban, A.N., McEwan, A. and Meshkinfamfard, S., (2019). 'Bringing the blessing of dimensionality to the edge'. 1st International Conference on Industrial Artificial Intelligence, 23-27 July. Shenyang, China. New York: IEEE, pp. 1-5.
JournalInternational Conference on Industrial Artificial Intelligence (IAI)
TypeMeetings and Proceedings
The following license files are associated with this item:
- Creative Commons