wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual demard wolffd@0: wolffd@0: wolffd@0: wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Automatic relevance determination using the MLP. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: demmlp1

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wolffd@0: Description wolffd@0:

wolffd@0: This script demonstrates the technique of automatic relevance wolffd@0: determination (ARD) using a synthetic problem having three input wolffd@0: variables: x1 is sampled uniformly from the range (0,1) and has wolffd@0: a low level of added Gaussian noise, x2 is a copy of x1 wolffd@0: with a higher level of added noise, and x3 is sampled randomly wolffd@0: from a Gaussian distribution. The single target variable is determined wolffd@0: by sin(2*pi*x1) with additive Gaussian noise. Thus x1 is wolffd@0: very relevant for determining the target value, x2 is of some wolffd@0: relevance, while x3 is irrelevant. The prior over weights is wolffd@0: given by the ARD Gaussian prior with a separate hyper-parameter for wolffd@0: the group of weights associated with each input. A multi-layer wolffd@0: perceptron is trained on this data, with re-estimation of the wolffd@0: hyper-parameters using evidence. The final values for the wolffd@0: hyper-parameters reflect the relative importance of the three inputs. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: demmlp1, demev1, mlp, evidence

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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