Scalable Learning for Dispersed Knowledge Systems
Charles Z. Liu, Manolya Kavakli · 2016
This paper mainly focuses on dealing with the issue of scalable learning in dispersed knowledge system. A scalable learning scheme and ξ process are proposed with a theoretical analysis. With the proposed scheme, the dispersed knowledge system can be used as a centralized system without knowing the overview of the global database. A case study of application in dispersed face recognition system is given to show how the proposed scheme implements and works.