The DOI matches its Crossref record; 131 of the 131 references it cites were found in a registry or an index.
Neural Networks: A Review from a Statistical Perspective
Open accessAbstract
This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and encourages cross-disciplinary research in the directions most likely to bear fruit. The areas of statistical interest are briefly outlined, and a series of examples indicates the flavor of ANN models. We then treat various topics in more depth. In each case, we describe the neural network architectures and training rules and provide a statistical commentary. The topics treated in this way are perceptrons (from single-unit to multilayer versions), Hopfield-type recurrent networks (including probabilistic versions strongly related to statistical physics and Gibbs distributions) and associative memory networks trained by so-called unsuperviszd learning rules. Perceptrons are shown to have strong associations with discriminant analysis and regression, and unsupervized networks with cluster analysis. The paper concludes with some thoughts on the future of the interface between neural networks and statistics.
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Cites
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Classification and Regression Trees.
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Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
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Information Theory and an Extension of the Maximum Likelihood Principle
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Density Estimation for Statistics and Data Analysis
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Cited by
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Forecasting with artificial neural networks:
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Neural networks and statistical techniques: A review of applications
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Closed-Loop Turbulence Control: Progress and Challenges
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Metadata
- Authors
- Cheng, B., & Titterington, D. M.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1994-02-01
- Volume
- 9
- Issue
- 1
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2076750860
- Cited by
- 1,048
- References
- 131
- FWCI
- 42.78
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
