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Neural network with unbounded activation functions is universal approximator
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Metadata
- Authors
- Sonoda, S., & Murata, N.
- Affiliations
- Waseda University (JP)
- Corresponding author
- Sonoda, Sho
- ORCID
- Murata, Noboru
- Venue
- Applied and Computational Harmonic Analysis
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 43
- Issue
- 2
- Pages
- 233-268
- ISSN
- 1063-5203 (ISSN-L)
- OpenAlex
- W2267236547
- Cited by
- 303
- References
- 36
- Citations by year
- 2026: 13 · 2025: 34 · 2024: 37 · 2023: 32 · 2022: 33 · 2021: 47 · 2020: 40 · 2019: 31 · 2018: 22 · 2017: 9
- FWCI
- 11.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Applied and Computational Harmonic Analysis (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- JSPS (15J07517)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- universal approximation, artificial neural networks, unbounded activation functions, rectified linear units, ridgelet transform, Fourier slice theorem, Radon transform, admissibility conditions, low-pass filtering, backpropagation
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