The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
How many hidden layers and nodes?
Abstract
The question of how many hidden layers and how many hidden nodes should there be always comes up in any classification task of remotely sensed data using neural networks. Until today there has been no exact solution. A method of shedding some light to this question is presented in this paper. A near‐optimal solution is discovered after searching with a genetic algorithm. A novel fitness function is introduced that concurrently seeks for the most accurate and compact solution. The proposed method is thoroughly compared to many other methods currently in use, including several heuristics and pruning algorithms. The results are encouraging, indicating that it is time to shift our focus from suboptimal practices to efficient search methods, to tune the parameters of neural networks.
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Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
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Perez, H., Tah, J. H. M., & Mosavi, A. (2019) · Sensors · doi:10.3390/s19163556 · Cited by 253
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Analyzing high speed rail passengers’ train choices based on new online booking data in China
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Okai, J., Paraschiakos, S., Beekman, M., Knobbe, A., & de Sa, C. R. (2019) · 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857288 · Cited by 29
Metadata
- Authors
- Stathakis, D.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2009-04-20
- Volume
- 30
- Issue
- 8
- Pages
- 2133-2147
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2070188654
- Cited by
- 432
- References
- 1
- FWCI
- 2.89
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
