The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Neural Networks and Related Methods for Classification
Abstract
SUMMARY: Feed-forward neural networks are now widely used in classification problems, whereas non-linear methods of discrimination developed in the statistical field are much less widely known. A general framework for classification is set up within which methods from statistics, neural networks, pattern recognition and machine learning can be compared. Neural networks emerge as one of a class of flexible non-linear regression methods which can be used to classify via regression. Many interesting issues remain, including parameter estimation, the assessment of the classifiers and in algorithm development.
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Cites
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Metadata
- Authors
- Ripley, B. D.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1994-09-01
- Volume
- 56
- Issue
- 3
- Pages
- 409-437
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W188554010
- Cited by
- 627
- References
- 10
- FWCI
- 47.87
- 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).
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