Name
Abstract | ||
|---|---|---|
This paper presents a procedure to implement an automatic system for supervised pattern recognition with an ongoing learning capability.The purpose is to continuously increase the knowledge of the system and, accordingly, to enhance its performance in classification tasks.The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the increase in computational load and with the peril of incorporating noisy data to the training sample.Experimental results confirm the improvement in classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/SIBGRAPI.2001.963037 | SIBGRAPI |
Keywords | Field | DocType |
computational load,nearest neighbor rule,classification task,ongoing learning capability,classification accuracy,ongoing learning,central classifier,supervised pattern recognition,noisy data,automatic system,data mining,prototypes,training data,terminology,computational complexity,learning artificial intelligence,robustness,data handling,neural networks,degradation,knowledge based systems,nearest neighbor,pattern recognition | k-nearest neighbors algorithm,Pattern recognition,Computer science,Knowledge-based systems,Robustness (computer science),Artificial intelligence,Artificial neural network,Classifier (linguistics),Group method of data handling,Knowledge acquisition,Machine learning,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
0-7695-1330-1 | 2 | 0.41 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R Barandela | 1 | 558 | 23.46 |
| Mariela Juárez | 2 | 2 | 0.41 |