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.
Recommended Citation
A. Akusok et al., "High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications," IEEE Access, vol. 3, pp. 1011 - 1025, article no. 7140733, Institute of Electrical and Electronics Engineers, Jan 2015.
The definitive version is available at https://doi.org/10.1109/ACCESS.2015.2450498
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
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
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2015
