Abstract
This paper presents an ensemble approach consisting of global SVM and local SVM. Global SVM is estimated according to its decision confidence. Local SVM handles the query whose global decision is of low confidence. Local SVM is constructed over query’s neighborhood, which is developed under the guidance of an informative metric. And its training is based on a query-based objective function. Global SVM helps to define the new metric. Some heuristics proposed to specify neighborhood size and hyper parameters. We present experimental evidence of classification performance improved by our schema over state of the arts on real datasets.
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References
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines, vol 10. Cambridge University Press, London, pp 381–388
Richard MD, Lippmann RP (1991) Neural network classifiers estimate bayesian a posteriori probabilities. Neural Comput 1(3):461–483
Craven M, DiPasquo D, Freitag D (1998) Learning to extract symbolic knowledge from the World Wide Web. In: Proceedings of 15th national conference on artificial intelligence, 8:880–886
Milgram J, Mohamed CSR (2005) Estimating accurate multi-class probabilities with support vector machines. In: Proceedings of IEEE international joint conference on neural networks, 3:1906–1911
Kwork TJ (1999) Moderate the outputs of support vector machine classifiers. IEEE Trans Neural Networks 10:1018–1032
Friedman JH (1994) Flexible metric nearest neighbor classification. In: Technical report, department of statistics, vol 6. Stanford University, pp 95–104
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© 2013 Springer-Verlag London
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Gao, D., Rong, XS., You, XY., Xu, M., Huo, F. (2013). Classifier Selection Based on Support Vector Technique. In: Du, W. (eds) Informatics and Management Science VI. Lecture Notes in Electrical Engineering, vol 209. Springer, London. https://doi.org/10.1007/978-1-4471-4805-0_2
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DOI: https://doi.org/10.1007/978-1-4471-4805-0_2
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4804-3
Online ISBN: 978-1-4471-4805-0
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