GENERALIZATION IN A 2-LAYER NEURAL-NETWORK
SCIE
SCOPUS
- Title
- GENERALIZATION IN A 2-LAYER NEURAL-NETWORK
- Authors
- KANG, KJ; KWON, C; OH, JH; PARK, Y
- Date Issued
- 1993-12
- Publisher
- AMERICAN PHYSICAL SOC
- Abstract
- Generalization in a fully connected two-layer neural network with N input nodes, M hidden nodes, a single output node, and binary weights is studied in the annealed approximation. When the number of examples is the order of N, the generalization error approaches a plateau and the system is in a permutation symmetric phase. When the number of examples is of the order of MN, the system undergoes a first-order phase transition to perfect generalisation and the permutation symmetry breaks. Results of the computer simulation show good agreement with analytic calculation
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/12328
- DOI
- 10.1103/PhysRevE.48.4805
- ISSN
- 1539-3755
- Article Type
- Article
- Citation
- PHYSICAL REVIEW E, vol. 48, no. 6, page. 4805 - 4809, 1993-12
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- There are no files associated with this item.
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