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Title: Cascading classifiers (English)
Author: Alpaydin, Ethem
Author: Kaynak, Cenk
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 34
Issue: 4
Year: 1998
Pages: [369]-374
Summary lang: English
Category: math
Summary: We propose a multistage recognition method built as a cascade of a linear parametric model and a $k$-nearest neighbor ($k$-NN) nonparametric classifier. The linear model learns a “rule” and the $k$-NN learns the “exceptions” rejected by the “rule.” Because the rule-learner handles a large percentage of the examples using a simple and general rule, only a small subset of the training set is stored as exceptions during training. Similarly during testing, most patterns are handled by the rule -learner and few are handled by the exception-learner thus causing only a small increase in memory and computation. A multistage method like cascading is a better approach than a multiexpert method like voting where all learners are used for all cases; the extra computation and memory for the second learner is unnecessary if we are sufficiently certain that the first one’s response is correct. We discuss how such a system can be trained using cross validation. This method is tested on the real-world application of handwritten digit recognition. (English)
Keyword: multistage recognition method
Keyword: linear parametric model
Keyword: cascading
MSC: 68T05
MSC: 68T10
idZBL: Zbl 1274.68284
Date available: 2009-09-24T19:17:16Z
Last updated: 2015-03-28
Stable URL:
Reference: [1] Alpaydın E.: 1997.REx: Learning A Rule and Exceptions. International Computer Science Institute TR-97-040 Berkeley
Reference: [2] Alpaydın E., Gürgen F.: Comparison of kernel estimators, perceptrons and radial–basis functions for OCR and speech classification.Neural Computing Appl. 3 (1995), 38–49 10.1007/BF01414175
Reference: [3] Bishop C. M.: Neural Networks for Pattern Recognition.Oxford University Press, Oxford 1995 Zbl 0868.68096, MR 1385195
Reference: [4] Garris M. D., Blue J. L., Candela G. T., Dimmick D. L., Geist J., Grother P. J., Janet S. A., Wilson C. L.: NIST Form–Based Handprint Recognition System, NISTIR 5469, 199.
Reference: [5] Pudil P., Novovičová J., Bláha S., Kittler J.: Multistage pattern recognition with reject option.In: 11th IAPR International Conference on Pattern Recognition B, 1992, vol. II, pp. 92–95
Reference: [6] Xu L., Krzyżak, A., Suen C. Y.: Methods of combining multiple classifiers and their applications to handwriting recognition.IEEE Trans. Systems Man Cybernet. 22 (1992), 418–435 10.1109/21.155943


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