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Royal Statistical Society
Statistical Computing Section
Meetings
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Support Vector Machines for Classification and Regression + Section AGM
Support Vector Machine (SVM) classifiers have recently been
the subject of intense research activity within the pattern
recognition and machine learning community. They are the
State-of-the-Art methods and faster than neural networks.
This meeting aims to introduce the methods associated with
SVM to the wider statistical community.
Sean Holden (University College London
)
"An introduction to Support Vector Machines"
Synopsis: To follow
Peter Sollich (Kings College London
)
"Probabilistic methods for Support Vector Machines"
Synopsis: I describe a framework for interpreting SVMs as maximum
a posteriori (MAP) solutions for inference problems with
Gaussian process priors. This can provide intuitive
guidelines for choosing a "good" SVM kernel. It can also
assign (by evidence maximization) optimal values for
parameters such as the choice level C, which cannot be
determined unambiguously from properties of the MAP
solution alone (such as cross-validation error). I
illustrate this using a simple approximate expression for
the SVM evidence. Once C has been determined, class
probabilities for SVM predictions can also be obtained.
Mike Tipping (Microsoft Research UK
)
"Variational Relevance Vector Machines"
Synopsis: There was none.
Report
- A Report on this meeting is available.
Date & Time
Wednesday 30th May 2001 at 2:00-5:00 pm
Place
Errol Street
Refreshments
There will be tea and biscuits served at 3:00 pm.
Enquiries
Sue Evans (020 7631 6441)
s.evans@bbk.ac.uk
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