The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
Open accessAbstract
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.
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Cites
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Neural Networks: A Comprehensive Foundation
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UCI Machine Learning Repository
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Book Review: Pattern Recognition and Machine Learning
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Extreme learning machine: Theory and applications
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The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
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(2006) · Springer eBooks · Cited by 8,123
Neural networks: A comprehensive foundation
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Metadata
- Authors
- Akusok, A., Bjork, K.-M., Miche, Y., & Lendasse, A.
- Venue
- IEEE Access
- Published
- 2015
- Volume
- 3
- Pages
- 1011-1025
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W1520795727
- Cited by
- 269
- References
- 42
- FWCI
- 31.59
- Type
- journal article
- Language
- en
- Open access
- gold
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- Akusok, A., Bjork, K.-M., Miche, Y., & Lendasse, A. (2015). High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access. https://doi.org/10.1109/access.2015.2450498
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
