Geodesic
Cascading classifiers
Kybernetika, Tome 34 (1998) no. 4, pp. 369-374 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

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.
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.
Classification : 68T05, 68T10
Keywords: multistage recognition method; linear parametric model; cascading 
@article{KYB_1998_34_4_a2,
     author = {Alpaydin, Ethem and Kaynak, Cenk},
     title = {Cascading classifiers},
     journal = {Kybernetika},
     pages = {369--374},
     year = {1998},
     volume = {34},
     number = {4},
     zbl = {1274.68284},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a2/}
}
TY  - JOUR
AU  - Alpaydin, Ethem
AU  - Kaynak, Cenk
TI  - Cascading classifiers
JO  - Kybernetika
PY  - 1998
SP  - 369
EP  - 374
VL  - 34
IS  - 4
UR  - http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a2/
LA  - en
ID  - KYB_1998_34_4_a2
ER  - 
%0 Journal Article
%A Alpaydin, Ethem
%A Kaynak, Cenk
%T Cascading classifiers
%J Kybernetika
%D 1998
%P 369-374
%V 34
%N 4
%U http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a2/
%G en
%F KYB_1998_34_4_a2
Alpaydin, Ethem; Kaynak, Cenk. Cascading classifiers. Kybernetika, Tome 34 (1998) no. 4, pp. 369-374. http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a2/

[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 | DOI

[3] Bishop C. M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford 1995 | MR | Zbl

[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. In: 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