PhD thesis available.

[Cyril Goutte]

Dear Connectionists,


I am pleased to announce that the manuscript of my thesis:

      STATISTICAL LEARNING AND REGULARISATION FOR REGRESSION


is available through the WWW at the following URL:

http://eivind.imm.dtu.dk/staff/goutte/PUBLIS/thesis.html


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Abstract :

This thesis deals with the use of statistical learning and
regularisation on regression problems, with a focus on time series
modelling and system identification. Both linear models and non-linear
neural networks are considered as particular modelling techniques.

Linear and non-linear parametric regression are briefly introduced and
their limit is shown using the bias-variance decomposition of the
generalisation error. We then show that as such, those problems are
ill-posed, and thus need to be regularised. Regularisation introduces
a number of hyper-parameters, the setting of which is performed by
estimating generalisation error. Several such methods are evoked in
the course of this work.

The use of these theoretical aspects is targeted towards two
particular problems. First an iterative method relying on
generalisation error to extract the relevant delays from time series
data is presented. Then a particular regularisation functional is
studied, that provides pruning of unnecessary parameters as well as a
regularising effect. This last part uses Bayesian estimators, and a
brief presentation of those estimators is also given in the thesis.

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	Cyril.

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Cyril Goutte |> cg at imm.dtu.dk <| Tel: +45-4525 3921  (Fax: +45-4587 2599)
Department of Mathematical Modelling - D.T.U., Bygn. 321 - DK-2800 Lyngby