Name
Abstract | ||
|---|---|---|
In this paper the ability of the functional networks approach to solve classification problems is explored. Functional networks were introduced by Castillo et al. [1] as an alternative to neural networks. They have the same purpose, but unlike neural networks, neural functions are learned instead of weights, using families of linear independent functions. This is illustrated by applying several models of functional networks to a set of simulated data and to the well-known Iris data and Pima Indian data sets. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_50 | ICANN (2) |
Keywords | Field | DocType |
linear independent function,neural network,pima indian data set,well-known iris data,simulated data,functional network,classification problem,neural function,linear independence | Linear independence,Data set,Computer science,Functional networks,Artificial intelligence,Formal methods,Iris flower data set,Artificial neural network,Pima Indian,Machine learning | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
2 | 0.39 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rosa Eva Pruneda | 1 | 28 | 4.21 |
| Beatriz Lacruz | 2 | 61 | 6.80 |
| Cristina Solares | 3 | 46 | 7.89 |