Gaussian Processes for Machine Learning

[Matthias Seeger]

Dear colleagues,

the tutorial paper
  Gaussian Processes for Machine Learning
will appear in the International Journal for Neural Systems (IJNS) and is
available from
  www.cs.berkeley.edu/~mseeger/papers/bayesgp-tut.html.
I hope this is interesting to some of you.

Best wishes, Matthias

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Abstract:
Gaussian processes (GPs) are natural generalisations of multivariate
Gaussian random variables to infinite (countably or continuous) index
sets. GPs have been applied in a large number of fields to a diverse
range of ends, and very many deep theoretical analyses of various
properties are available. This paper gives an introduction to Gaussian
processes on a fairly elementary level with special emphasis on
characteristics
relevant in machine learning. It draws explicit connections to branches
such as
spline smoothing models and support vector machines in which similar ideas
have
been investigated.

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Matthias Seeger              Tel: 510-642-8468
485 Soda Hall, UC Berkeley   Fax: 510-642-5775
Berkeley, CA 94720-1776      www.cs.berkeley.edu/~mseeger