Name
Title | ||
|---|---|---|
Hierarchical Classifiers based on Neighbourhood Criteria with Adaptive Computational Cost |
Abstract | ||
|---|---|---|
Classifiers based on neighbourhood concept require a high computational cost when the Reference Patterns Set is large. In this paper, we propose the use of hierarchical classifiers to reduce this computational cost, maintaining the hit rate in the recognition of handwritten digits. The hierarchical classifiers reach the hit rate of the best individual classifier. We have used NIST Database to carry out the experimentation, and we have worked with two test sets: in Test 1 (SD3, SD19) the hit rate is 99.54%, with a speed-up of 40.6, and in Test 2 (SD7), the hit rate is 97.51% with a speed-up of 15.7. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0031-3203(01)00243-6 | Pattern Recognition |
Keywords | DocType | Volume |
handwritten digits,ocr,hierarchical classifiers,adaptive computational cost,k -ncn classifier,neighbourhood criteria,k -nn classifier | Conference | 35 |
Issue | ISSN | ISBN |
12 | Pattern Recognition | 972-98050-3-2 |
Citations | PageRank | References |
5 | 0.46 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. Rodríguez | 1 | 5 | 0.46 |
| I. Soraluze | 2 | 12 | 1.61 |
| Javier Muguerza | 3 | 338 | 28.61 |
| J. I. Martín | 4 | 6 | 1.18 |
| G. Álvarez | 5 | 5 | 0.46 |