Name
Abstract | ||
|---|---|---|
In this paper, supposing a Gaussian noise model, we study the problem of multi-output nonlinear regression using multilayer perceptrons (MLP) when the noise in each output is a correlated autoregressive time series and there is a spatial correlation between different output noise sources. We show that using a maximum likelihood approach, the noise parameters can be determined simultaneously with the network weights and used to improve the network generalization performance. For two special cases of first and second order AR noise, the appropriate cost functions to minimize them are derived. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/ICASSP.2000.860131 | ICASSP |
Keywords | Field | DocType |
network weight,correlated autoregressive time series,network generalization performance,multi-output neural network,spatio-temporal correlated noise,noise parameter,order ar noise,appropriate cost function,gaussian noise model,different output noise source,maximum likelihood approach,multi-output nonlinear regression,spatial correlation,maximum likelihood,linear regression,cost function,gaussian noise,multilayer perceptron,intelligent networks,random variables,maximum likelihood estimation,neural network,nonlinear regression,second order,time series,neural networks,colored noise | Autoregressive model,Value noise,Spatial correlation,Noise measurement,Pattern recognition,Computer science,Nonlinear regression,Artificial intelligence,Artificial neural network,Perceptron,Gaussian noise | Conference |
ISSN | ISBN | Citations |
1520-6149 | 0-7803-6293-4 | 1 |
PageRank | References | Authors |
0.37 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Hosseini | 1 | 1 | 0.37 |
| Christian Jutten | 2 | 450 | 39.98 |