Title :
High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
Author :
Akusok, Anton ; Bjork, Kaj-Mikael ; Miche, Yoan ; Lendasse, Amaury
Author_Institution :
Dept. of Mech. & Ind. Eng., Univ. of Iowa, Iowa City, IA, USA
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.
Keywords :
Big Data; learning (artificial intelligence); linear algebra; Big Data; ELM Toolbox; extreme learning machines; feedforward neural networks; linear algebraic performance; Learning systems; Machine learning; Performance evaluation; Artificial neural networks; Computer applications; Feedforward neural networks; High performance computing Software; Learning systems; Machine learning; Neural networks; Open source software; Performance analysis; Prediction methods; Predictive models; Radial basis function networks; Scientific computing; Supervised learning; Utility programs;
Journal_Title :
Access, IEEE
DOI :
10.1109/ACCESS.2015.2450498
