The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multiple network fusion using fuzzy logic
Abstract
Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output, with subjective evaluation of the importance of the individual neural networks. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.
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Citations
Cites
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Cited by
Ensembles of Learning Machines
Valentini, G., & Masulli, F. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45808-5_1 · Cited by 276
Metadata
- Authors
- Cho, S.-B., & Kim, J. H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1995-03
- Volume
- 6
- Issue
- 2
- Pages
- 497-501
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2098351657
- Cited by
- 197
- References
- 2
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multiple network fusion using fuzzy logic. https://app.cochat.ai/papers/10.1109/72.363487
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- Cho, S.-B., & Kim, J. H. (1995). Multiple network fusion using fuzzy logic. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.363487
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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.
