Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demev2 Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demev2 Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Demonstrate Bayesian classification for the MLP. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

Daniel@0: 
Daniel@0: demev2
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Daniel@0: Description Daniel@0:

Daniel@0: A synthetic two class two-dimensional dataset x is sampled Daniel@0: from a mixture of four Gaussians. Each class is Daniel@0: associated with two of the Gaussians so that the optimal decision Daniel@0: boundary is non-linear. Daniel@0: A 2-layer Daniel@0: network with logistic outputs is trained by minimizing the cross-entropy Daniel@0: error function with isotroipc Gaussian regularizer (one hyperparameter for Daniel@0: each of the four standard weight groups), using the scaled Daniel@0: conjugate gradient optimizer. The hyperparameter vectors alpha and Daniel@0: beta are re-estimated using the function evidence. A graph Daniel@0: is plotted of the optimal, regularised, and unregularised decision Daniel@0: boundaries. A further plot of the moderated versus unmoderated contours Daniel@0: is generated. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: evidence, mlp, scg, demard, demmlp2
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: