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Cascading classifiers. (English) Zbl 1274.68284
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.

##### MSC:
 68T05 Learning and adaptive systems in artificial intelligence 68T10 Pattern recognition, speech recognition
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##### References:
 [1] Alpaydın E.: 1997. REx: Learning A Rule and Exceptions. International Computer Science Institute TR-97-040 Berkeley [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 · Zbl 05473699 · doi:10.1007/BF01414175 [3] Bishop C. M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford 1995 · Zbl 0868.68096 [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. [5] Pudil P., Novovičová J., Bláha S., Kittler J.: Multistage pattern recognition with reject option. 11th IAPR International Conference on Pattern Recognition B, 1992, vol. II, pp. 92-95 [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 · doi:10.1109/21.155943
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