High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Anton Akusok, Kaj-Mikael Björk, Yoan Miche, Amaury Lendasse

Research output: Contribution to journalArticleScientificpeer-review

149 Citations (Scopus)

Abstract

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.
Original languageUndefined/Unknown
Peer-reviewed scientific journalIEEE Access
Volume3
Pages (from-to)1011-1025
Number of pages15
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • Learning systems
  • Performance evaluation
  • Machine learninf

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