Name
Abstract | ||
|---|---|---|
In previous work we have derived a magnitude termed the 'Mean Squared Sensitivity' (MSS) to predict the performance degradation of a MLP affected by perturbations in different parameters. The present Letter continues the same line of researching, applying a similar methodology to deal with RBF networks and to study the implications when they are affected by input noise. We obtain the corresponding analytical expression for MSS in RBF networks and validate it experimentally, using two different models for perturbations: an additive and a multiplicative model. We discuss the relationship between MSS and the generalization ability. MSS is proposed as a quantitative measurement to evaluate the noise immunity and generalization ability of a RBFN configuration, giving even more generalization to our approach. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1023/A:1026275522974 | Neural Processing Letters |
Keywords | Field | DocType |
generalization,Mean Square Error degradation,noise immunity,perturbation models,Radial Basis Function | Magnitude (mathematics),Square (algebra),Radial basis function,Multiplicative model,Artificial intelligence,Noise immunity,Mathematics,Perturbation (astronomy),Machine learning | Journal |
Volume | Issue | ISSN |
18 | 1 | 1573-773X |
Citations | PageRank | References |
11 | 0.58 | 17 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. L. Bernier | 1 | 44 | 3.95 |
| A. F. Díaz | 2 | 24 | 2.45 |
| F. J. Fernández | 3 | 17 | 3.33 |
| A. Cañas | 4 | 41 | 2.18 |
| Jesús González | 5 | 604 | 44.40 |
| P. Martín-Smith | 6 | 29 | 4.04 |
| J. Ortega | 7 | 940 | 73.05 |