The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Feed-forward neural networks
Open accessAbstract
One critical aspect neural network designers face today is choosing an appropriate network size for a given application. Network size involves in the case of layered neural network architectures, the number of layers in a network, the number of nodes per layer, and the number of connections. Roughly speaking, a neural network implements a nonlinear mapping of u=G(x). The mapping function G is established during a training phase where the network learns to correctly associate input patterns x to output patterns u. Given a set of training examples (x, u), there is probably an infinite number of different size networks that can learn to map input patterns x into output patterns u. The question is, which network size is more appropriate for a given problem? Unfortunately, the answer to this question is not always obvious. Many researchers agree that the quality of a solution found by a neural network depends strongly on the network size used. In general, network size affects network complexity, and learning time. It also affects the generalization capabilities of the network; that is, its ability-to produce accurate results on patterns outside its training set.>
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Cites
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Designing neural networks using genetic algorithms
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The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks
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Artificial Neural Networks: Approximation and Learning Theory
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Cited by
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
A comprehensive review on ensemble deep learning: Opportunities and challenges
Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127
How effective is the Grey Wolf optimizer in training multi-layer perceptrons
Mirjalili, S. (2015) · Applied Intelligence · doi:10.1007/s10489-014-0645-7 · Cited by 677
Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342
Security, Privacy, and Usability in Continuous Authentication: A Survey
Baig, A. F., & Eskeland, S. (2021) · Sensors · doi:10.3390/s21175967 · Cited by 98
Metadata
- Authors
- Bebis, G., & Georgiopoulos, M.
- Venue
- IEEE Potentials
- Published
- 1994-10
- Volume
- 13
- Issue
- 4
- Pages
- 27-31
- ISSN
- 0278-6648 (ISSN-L)
- OpenAlex
- W1986760892
- Cited by
- 746
- References
- 9
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
