Name
Abstract | ||
|---|---|---|
The two more known paradigms of Neural Networks are usually considered as very different structures. In this paper both structures are studied from the point of view of a general formal hierarchical recursive framework for describing connectionist models. As a result of this study both paradigms are defined, using the same language, from a top level of abstraction, down to a level suitable for implementation. The final basic primitives of the descriptions are taken from a set of standard building blocks in computation. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1007/BFb0032549 | international work-conference on artificial and natural neural networks |
Keywords | Field | DocType |
Structural Level Comparison,Neural Computation,Basic Paradigms | Abstraction,Computer science,Binary tree,Models of neural computation,Theoretical computer science,Artificial intelligence,Artificial neural network,Connectionism,Machine learning,Recursion,Structural level,Computation | Conference |
Volume | ISSN | ISBN |
1240 | 0302-9743 | 3-540-63047-3 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José R. Álvarez | 1 | 487 | 59.45 |
| JR Alvarez | 2 | 0 | 0.34 |