Name
Abstract | ||
|---|---|---|
Inductive learning can be used to extract rules required for an expert system which assists in output analysis for system simulation. However, several expamples of the system, constituting an instance set, are required for learning to take place. Generating the required instance set to be used by an inductive learning algorithm is time consuming and complex. This paper is an attempt to clarify this problem, discuss its complexity, and suggest context related solutions. A procedure for automatic instance generation is then proposed. The proposed procedure is a combination of three search methods (grid based, forward search, backward search). |
Year | DOI | Venue |
|---|---|---|
1994 | 10.5555/193201.194917 | Winter Simulation Conference |
Keywords | Field | DocType |
inductive learning,automatic instance generation,expert system,expert systems | Inductive bias,Instance-based learning,Multi-task learning,Simulation,Computer science,Expert system,Learning by example,Artificial intelligence,Data driven animation,Machine learning,Grid | Conference |
ISBN | Citations | PageRank |
0-7803-2109-X | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sima Parisay | 1 | 0 | 0.34 |
| Behrokh Khoshnevis | 2 | 125 | 14.25 |