The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Fast training algorithms for multilayer neural nets
Abstract
An algorithm that is faster than back-propagation and for which it is not necessary to specify the number of hidden units in advance is described. The relationship with other fast pattern-recognition algorithms, such as algorithms based on k-d trees, is discussed. The algorithm has been implemented and tested on artificial problems, such as the parity problem, and on real problems arising in speech recognition. Experimental results, including training times and recognition accuracy, are given. Generally, the algorithm achieves accuracy as good as or better than nets trained using back-propagation. Accuracy is comparable to that for the nearest-neighbor algorithm, which is slower and requires more storage space.
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
McClelland, J. L., Rumelhart, D. E., & AU (1987) · doi:10.7551/mitpress/5237.001.0001 · Cited by 3,315
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002
Cited by
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Murthy, S. K. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009744630224 · Cited by 991
Metadata
- Authors
- Brent, R. P.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1991-05
- Volume
- 2
- Issue
- 3
- Pages
- 346-354
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2110469359
- Cited by
- 177
- References
- 3
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- closed
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