Name
Abstract | ||
|---|---|---|
This paper presents some ideas about automatic procedures to implement a system with the capability of detecting patterns arising from classes not represented in the training sample. The procedure aims at incorporating automatically to the training sample the necessary information about the new class for correctly recognizing patterns from this class in future classification tasks. The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the peril of incorporating noisy data to the training sample. Experimental results with real data confirm the benefits of the proposed procedure. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-70659-3_54 | SSPR/SPR |
Keywords | Field | DocType |
nearest neighbor rule,central classifier,new class,future classification task,proposed procedure,noisy data,incomplete training samples,supervised pattern recognition,automatic procedure,training sample,pattern recognition,nearest neighbor | k-nearest neighbors algorithm,Anomaly detection,Noisy data,Nearest neighbour,Pattern recognition,Computer science,Information extraction,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-44011-9 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R Barandela | 1 | 558 | 23.46 |
| F J. Ferri | 2 | 293 | 22.43 |
| T. Nájera | 3 | 0 | 0.34 |