Name
Abstract | ||
|---|---|---|
Accompanying the application of rule extraction algorithms to real-world problems is the crucial difficulty to compile a representative data set. Domain experts often find it difficult to identify all input parameters that have an influence on the outcome of the problem. In this paper we discuss the problem of identifying relevant input parameters from a set of potential input parameters. We show that sensitivity analysis applied to a trained feedforward neural network is an efficient tool for... |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1007/3-540-59497-3_199 | IWANN |
Keywords | Field | DocType |
sensitivity analysis,feedforward neural network | Feedforward neural network,Pattern recognition,Computer science,Extraction algorithm,Compiler,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
930 | 0302-9743 | 3-540-59497-3 |
Citations | PageRank | References |
20 | 1.48 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andries Petrus Engelbrecht | 1 | 2183 | 125.32 |
| Ian Cloete | 2 | 132 | 16.61 |
| Jacek M. Zurada | 3 | 2553 | 226.22 |