The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A combined SVM and LDA approach for classification
Abstract
This paper describes a new large margin classifier, named SVM/LDA. This classifier can be viewed as an extension of support vector machine (SVM) by incorporating some global information about the data. The SVM/LDA classifier can be also seen as a generalization of linear discriminant analysis (LDA) by incorporating the idea of (local) margin maximization into standard LDA formulation. We show that existing SVM software can be used to solve the SVM/LDA formulation. We also present empirical comparisons of the proposed algorithm with SVM and LDA using both synthetic and real world benchmark data.
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Cites
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Metadata
- Authors
- Xiong, T., & Cherkassky, V.
- Venue
- Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
- Publisher
- IEEE
- Published
- 2006-01-05
- Volume
- 3
- Pages
- 1455-1459
- OpenAlex
- W1485231155
- Cited by
- 980
- References
- 2
- FWCI
- 172.49
- Type
- conference paper
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
