Name
Abstract | ||
|---|---|---|
Both symbolic knowledge representation systems and artificial neural networks play a significant role in Artificial Intelligence. A recent trend in the field aims at interweaving these techniques, in order to improve robustness and performance of classification and clustering systems. In this paper, we present a novel architecture based on the connectionist adaptation of ontological knowledge. The proposed architecture was used effectively to improve image segment classification within a multimedia application scenario. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04277-5_47 | ICANN (2) |
Keywords | Field | DocType |
clustering system,novel architecture,multimedia application scenario,image segment classification,connectionist models,symbolic knowledge representation system,connectionist adaptation,artificial intelligence,ontological knowledge,artificial neural network,formal knowledge adaptation,proposed architecture,artificial intelligent,neural network,image segmentation,knowledge representation | Neuro-fuzzy,Knowledge representation and reasoning,Semantic reasoner,Computer science,Description logic,Robustness (computer science),Artificial intelligence,Artificial neural network,Cluster analysis,Machine learning,Connectionism | Conference |
Volume | ISSN | Citations |
5769 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilianna Kollia | 1 | 100 | 9.71 |
| Nikolaos Simou | 2 | 39 | 3.29 |
| Giorgos Stamou | 3 | 1200 | 76.88 |
| Andreas Stafylopatis | 4 | 378 | 53.30 |