This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The results are applicable to a wide range of machine learning problems and thus provide a solid ground for tackling numerous Big Data challenges. The included toolbox is targeted at enabling the full potential of ELMs to the widest range of users.
Call for papers- IEEE Aerospace and Electronic Systems Magazine - Special issue on: Selected Methods and Instrumentation of Metrology for Aerospace
IEEE Aerospace and Electronic Systems Magazine ; 32 , 5 ; 62
2017-05-01
1298963 byte
Article (Journal)
Electronic Resource
English
Call for papers IEEE aerospace and electronic systems magazine special issue on avionics systems
IEEE | 2013
Call for papers IEEE aerospace and electronic systems magazine special issue on avionics systems
IEEE | 2013