Name
Abstract | ||
|---|---|---|
We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.eswa.2005.11.020 | Expert Systems with Applications |
Keywords | Field | DocType |
Machine learning,Neural network,Case-based reasoning,Knowledge base,Personalization,Cosmetic industry | Data mining,Feature vector,Computer science,Expert system,Metric (mathematics),Time delay neural network,Artificial intelligence,Case-based reasoning,Artificial neural network,Hybrid system,Machine learning,Personalization | Journal |
Volume | Issue | ISSN |
32 | 1 | 0957-4174 |
Citations | PageRank | References |
20 | 0.86 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kwang Hyuk Im | 1 | 75 | 6.31 |
| Sang Chan Park | 2 | 481 | 42.12 |