Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demev3 Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demev3 Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Demonstrate Bayesian regression for the RBF. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

Daniel@0: 
Daniel@0: demev3
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Daniel@0: Description Daniel@0:

Daniel@0: The problem consists an input variable x which sampled from a Daniel@0: Gaussian distribution, and a target variable t generated by Daniel@0: computing sin(2*pi*x) and adding Gaussian noise. An RBF Daniel@0: network with linear outputs is trained by minimizing a sum-of-squares Daniel@0: error function with isotropic Gaussian regularizer, using the scaled Daniel@0: conjugate gradient optimizer. The hyperparameters alpha and Daniel@0: beta are re-estimated using the function evidence. A graph Daniel@0: is plotted of the original function, the training data, the trained Daniel@0: network function, and the error bars. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: demev1, evidence, rbf, scg, netevfwd
Daniel@0: Pages: Daniel@0: Index Daniel@0:
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Copyright (c) Ian T Nabney (1996-9) Daniel@0: Daniel@0: Daniel@0: Daniel@0: