Name
Abstract | ||
|---|---|---|
Artificial neural networks are powerful classification mechanisms. Neural networks encode knowledge in a set of numerical weights and biases. This data driven aspect of neural networks allows easy adjustments when change of environments or events occur. Numeric weights, however, are difficult to interpret in terms of rules, making it difficult for a human to understand what the neural network has learned. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1007/3-540-59497-3_229 | IWANN |
Keywords | Field | DocType |
extracting dnf rules,artificial neural networks,neural network,artificial neural network | Nervous system network models,Neuro-fuzzy,Physical neural network,Computer science,Recurrent neural network,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Deep learning,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
930 | 0302-9743 | 3-540-59497-3 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. L. Viktor | 1 | 11 | 2.59 |
| Ian Cloete | 2 | 132 | 16.61 |