The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Combining multiple neural networks by fuzzy integral for robust classification
Abstract
In the area of artificial neural networks, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. The authors propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters confirm the superiority of the presented method to the other voting techniques.>
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Cites
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 Systems, Man, and Cybernetics
- Published
- 1995
- Volume
- 25
- Issue
- 2
- Pages
- 380-384
- ISSN
- 0018-9472 (ISSN-L)
- OpenAlex
- W2159033850
- Cited by
- 340
- References
- 1
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cho, S.-B., & Kim, J. H. (1995). Combining multiple neural networks by fuzzy integral for robust classification. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/21.364825
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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.
