@TECHREPORT\{IMM2002-01261,
    author       = "J. Quiñonero-Candela",
    title        = "Extended Linear Models with Gaussian Priors",
    year         = "2002",
    keywords     = "linear models, bayes, Gaussian processes, Relevance Vector Machine, Expectation-Maximization algorithm",
    number       = "",
    series       = "",
    institution  = "Informatics and Mathematical Modelling, Technical University of Denmark",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kongens Lyngby, Denmark",
    type         = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/1261-full.html",
    abstract     = "In extended linear models the input space is projected onto a feature space by
means of an arbitrary non-linear transformation. A linear model is then applied to
the feature space to construct the model output. The dimension of the feature
space can be very large, or even infinite, giving the model a very big flexibility.
Support Vector Machines (SVM's) and Gaussian processes are two examples of
such models. In this technical report I present a model in which the dimension of
the feature space remains finite, and where a Bayesian approach is used to train
the model with Gaussian priors on the parameters. The Relevance Vector
Machine, introduced by Tipping, is a particular case of such a model. I give the
detailed derivations of the expectation-maximisation (EM) algorithm used in the
training. These derivations are not found in the literature, and might be helpful for
newcomers."
}