Name
Title | ||
|---|---|---|
Performance comparisons between backpropagation networks and classification trees on three real-world applications |
Abstract | ||
|---|---|---|
Multi-layer perceptrons and trained classification trees are two very different techniques which have recently become popular. Given enough data and time, both methods are capable of performing arbi(cid:173) trary non-linear classification. We first consider the important differences between multi-layer perceptrons and classification trees and conclude that there is not enough theoretical basis for the clear(cid:173) cut superiority of one technique over the other. For this reason, we performed a number of empirical tests on three real-world problems in power system load forecasting, power system security prediction, and speaker-independent vowel identification. In all cases, even for piecewise-linear trees, the multi-layer perceptron performed as well as or better than the trained classification trees. |
Year | Venue | Keywords |
|---|---|---|
1989 | NIPS | classification tree,backpropagation |
Field | DocType | ISBN |
Computer science,Electric power system,Power system security,Load forecasting,Artificial intelligence,Vowel,Backpropagation,Perceptron,Machine learning | Conference | 1-55860-100-7 |
Citations | PageRank | References |
16 | 6.00 | 2 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Les E. Atlas | 1 | 436 | 82.22 |
| Ronald A. Cole | 2 | 686 | 187.46 |
| Jerome T. Connor | 3 | 16 | 6.00 |
| Mohamed A. El-sharkawi | 4 | 391 | 46.23 |
| Robert J. Marks II | 5 | 274 | 62.56 |
| Yeshwant K. Muthusamy | 6 | 136 | 24.25 |
| Etienne Barnard | 7 | 438 | 57.85 |