The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Constructive algorithms for structure learning in feedforward neural networks for regression problems
Abstract
In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state-space search, we first describe the general issues in constructive algorithms, with special emphasis on the search strategy. A taxonomy, based on the differences in the state transition mapping, the training algorithm, and the network architecture, is then presented.
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Cites
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Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
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Trends in extreme learning machines: A review
Huang, G., Huang, G.-B., Song, S., & You, K. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.001 · Cited by 1,732
Neural networks in geophysical applications
van der Baan, M., & Jutten, C. (2000) · Geophysics · doi:10.1190/1.1444797 · Cited by 342
A constructive algorithm for training cooperative neural network ensembles
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Metadata
- Authors
- Kwok, T.-Y., & Yeung, D.-Y.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1997-05
- Volume
- 8
- Issue
- 3
- Pages
- 630-645
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2117941247
- Cited by
- 493
- References
- 108
- FWCI
- 13.79
- Type
- journal article
- Language
- en
- Open access
- closed
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