Name
Abstract | ||
|---|---|---|
Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1023/B:APIN.0000043559.83167.3d | Appl. Intell. |
Keywords | DocType | Volume |
local feature weighting,case-based reasoning,neural network,hybrid system | Journal | 21 |
Issue | ISSN | Citations |
3 | 1573-7497 | 13 |
PageRank | References | Authors |
0.64 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae Heon Park | 1 | 31 | 2.74 |
| Kwang Hyuk Im | 2 | 75 | 6.31 |
| Chung-Kwan Shin | 3 | 43 | 2.71 |
| Sang Chan Park | 4 | 481 | 42.12 |