Learning and generalization in higher-order perceptrons
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SCOPUS
- Title
- Learning and generalization in higher-order perceptrons
- Authors
- Yoon, H; Oh, JH
- Date Issued
- 1998-05
- Publisher
- TAYLOR & FRANCIS LTD
- Abstract
- We study learning from examples in higher-order perceptrons, which can realize polynomially separable rules. We first calculate the storage capacity of random binary patterns. It is found that the storage capacity is a monotonically increasing function of the relative weight parameter of the highest-order monomial term. We also analyse the generalization ability of higher-order perceptrons when they are trained by examples drawn from a realizable rule. Unlike their first-order counterparts, high-order perceptrons are found to exhibit stepwise learning as a function of the number of training examples. © 1998 Taylor and Francis Group, LLC.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/20783
- DOI
- 10.1080/13642819808205048
- ISSN
- 0141-8637
- Article Type
- Article
- Citation
- PHILOSOPHICAL MAGAZINE B-PHYSICS OF CONDENSED MATTER STATISTICAL MECHANICS ELECTRONIC OPTICAL AND MAGNETIC PROPERTIES, vol. 77, no. 5, page. 1557 - 1563, 1998-05
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