Name
Abstract | ||
|---|---|---|
A self-organizing neural tree is studied in this paper. The neural tree is suited to hierarchical classification and vector quantization. Unsupervised learning algorithms have been developed for the neural tree. The neural tree has been applied to speech recognition and image coding and the results are promising. A significant advantage of the neural tree is that its training is much shorter (at least an order of magnitude) than other frequency sensitive competitive networks and Kohonen topological maps. |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1016/0925-2312(93)90032-X | Neurocomputing |
Keywords | Field | DocType |
Neural tree,hierarchical classification,vector quantization,unsupervised learning,pattern recognition,data compression | Competitive learning,Pattern recognition,Learning vector quantization,Self-organizing map,Types of artificial neural networks,Time delay neural network,Vector quantization,Artificial intelligence,Deep learning,Mathematics,Neural gas,Machine learning | Journal |
Volume | Issue | ISSN |
5 | 2-3 | 0925-2312 |
Citations | PageRank | References |
8 | 0.81 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Li | 1 | 29 | 3.39 |
| Luyuan Fang | 2 | 48 | 9.89 |
| Ken Q-Q. Li | 3 | 8 | 0.81 |