| Presentation | 1998/3/19 An Incremental Learning method for GRBF with Retrieving of Interfered Patterns : Application for Case Based Reasonings Koichiro YAMAUCHI, Naohiro ISHII, |
|---|---|
| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper proposes a low-cost incremental learning method of Generalized Radial Basis Function (GRBF) for a Case Based Reasoning (CBR) system. A CBR system is one type of reasoning system that uses past cases for solving new problems. The system re-uses the solution of a case, which is the most similar to the new problem in a case database, to solve the new problem. If the solution is not good, it is revised by an expert who gives a correct solution. The problem and the correct solution are appended to the case database as a new case. If a system uses a neural network as its case database, the reasoning process is performed more effectively. The system only has to compute the activation of each element of the neural network. Then, an appropriate solution appears in the output layer. The neural network in the CBR system, however, must learn the new case incrementally without forgetting learned instances. To realize this ability, in this paper, we refine on our incremental learning system proposed in [1] so as to reduce the computational complexity. In the new method, the system make learn the Generalized Radial Basis Function (GRBF) [2] both the new case and some old cases which are predicted to be interfered by the learning. The computer simulation result shows that the generalization ability of our system is higher than k-Nearest Neighbors and the number of hidden units are smaller than that of the k-Nearest Neighbors. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Generalized Radial Basis Function(GRBF) / Resouce Allocating Network(RAN) / k-Nearest Neighbor(k-NN) / Incremental Learning / Case Based Reasoning(CBR) |
| Paper # | |
| Date of Issue |
| Conference Information | |
| Committee | NC |
|---|---|
| Conference Date | 1998/3/19(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | |
| Vice Chair | |
| Secretary | |
| Assistant | |
| Paper Information | |
| Registration To | Neurocomputing (NC) |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Incremental Learning method for GRBF with Retrieving of Interfered Patterns : Application for Case Based Reasonings |
| Sub Title (in English) | |
| Keyword(1) | Generalized Radial Basis Function(GRBF) |
| Keyword(2) | Resouce Allocating Network(RAN) |
| Keyword(3) | k-Nearest Neighbor(k-NN) |
| Keyword(4) | Incremental Learning |
| Keyword(5) | Case Based Reasoning(CBR) |
| 1st Author's Name | Koichiro YAMAUCHI |
| 1st Author's Affiliation | Department of Intelligence and Computer Science, Nagoya Institute of Technology() |
| 2nd Author's Name | Naohiro ISHII |
| 2nd Author's Affiliation | Department of Intelligence and Computer Science, Nagoya Institute of Technology |
| Date | 1998/3/19 |
| Paper # | |
| Volume (vol) | vol.97 |
| Number (no) | 623 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |