Name
Abstract | ||
|---|---|---|
The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples Several approaches are focused to form training data sets by identification of border examples or core examples with the aim to improve the accuracy of network classification and generalization However, a refinement of data sets by the elimination of outliers examples may increase the accuracy too In this paper, we analyze the use of different editing schemes based on nearest neighbor rule on the most popular neural networks architectures. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13278-0_39 | ISNN (1) |
Keywords | Field | DocType |
training example,edited nearest neighbor rule,border example,neural networks classification,core example,training data set,different editing scheme,artificial neural network,nearest neighbor rule,popular neural networks architecture,critical stage,nearest neighbor,neural network,accuracy,neural networks | k-nearest neighbors algorithm,Data set,Computer science,Outlier,Time delay neural network,Artificial intelligence,Artificial neural network,Training data sets,Network classification,Machine learning | Conference |
Volume | ISSN | ISBN |
6063 | 0302-9743 | 3-642-13277-4 |
Citations | PageRank | References |
2 | 0.36 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Alejo | 1 | 158 | 10.40 |
| J. M. Sotoca | 2 | 36 | 4.72 |
| R. M. Valdovinos | 3 | 193 | 13.67 |
| P. Toribio | 4 | 5 | 1.43 |