The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning, invariance, and generalization in high-order neural networks
Abstract
High-order neural networks have been shown to have impressive computational, storage, and learning capabilities. This performance is because the order or structure of a high-order neural network can be tailored to the order or structure of a problem. Thus, a neural network designed for a particular class of problems becomes specialized but also very efficient in solving those problems. Furthermore, a priori knowledge, such as geometric invariances, can be encoded in high-order networks. Because this knowledge does not have to be learned, these networks are very efficient in solving problems that utilize this knowledge.
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Cites
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
Elements of Artificial Neural Networks
Mehrotra, K., Mohan, C., & Ranka, S. (1996) · The MIT Press eBooks · doi:10.7551/mitpress/2687.001.0001 · Cited by 912
Enhanced random search based incremental extreme learning machine
Huang, G.-B., & Chen, L. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.10.008 · Cited by 891
Johnson, J. L., & Padgett, M. L. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761706 · Cited by 586
Shin, Y., & Ghosh, J. (2002) · IJCNN-91-Seattle International Joint Conference on Neural Networks · doi:10.1109/ijcnn.1991.155142 · Cited by 299
Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation
Remedios, S. W., Roy, S., Bermudez, C., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2020) · Medical Physics · doi:10.1002/mp.13880 · Cited by 37
Metadata
- Authors
- Giles, C. L., & Maxwell, T.
- Venue
- Applied Optics
- Publisher
- Optica Publishing Group
- Published
- 1987-12-01
- Volume
- 26
- Issue
- 23
- Pages
- 4972
- ISSN
- 0003-6935 (ISSN-L)
- 1539-4522
- OpenAlex
- W2068777106
- Cited by
- 676
- References
- 1
- FWCI
- 16.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Giles, C. L., & Maxwell, T. (1987). Learning, invariance, and generalization in high-order neural networks. Applied Optics. https://doi.org/10.1364/ao.26.004972
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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.
