| 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:
|
http://hdl.handle.net/10338.dmlcz/135217 |
| . |
| 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 |
| . |
