Applets & Web Software
The SVM Applet
Support Vector Machines are learning machines that can perform
binary classification (pattern recognition) and real valued function
approximation (regression estimation) tasks. Support Vector Machines
non-linearly map their n-dimensional input space into a high dimensional
feature space. In this high dimesional feature space a linear classifier
The CLRC has implemented a Java Applet which demonstrates the potential
of the SVM approach. The Applet can be downloaded from this
page, or if you just want to see what it can do you can use
the links below to run the programme for:
A new technique for "hedging" predictions was presented and discussed
recently by Alexander Gammerman and Vladimir Vovk at a special meeting of the British Computer Society. The method can be applied to many algorithms, including Support Vector Machines, Kernel Ridge Regression,
Kernel Nearest Neighbours and other state-of-the-art methods.
The hedged predictions include confidence measures that are provably
valid and it becomes possible to control the number of errors by selecting a
suitable confidence level.
The discussants of the technique included Vladimir Vapnik, Alexey
Chervonenkis, Glenn Shafer, Zhiyuan Luo and many others.
The paper and the discussion can be found
Our Applet is sponsored by
The SVM website is implemented by
SVM Applet Feedback
"Nice visualisation technique."
Dr Michael Thess, Prudential Systems Software GmbH
"This is a great demo...I was trying to compare it with
other classification techniques such as Discriminant Analysis, and
it does show what SVs can do really well."
Aravind Ganapathiraju, Mississippi State University
"Congratulations! Your applet could be useful for teaching
Giorgio Valentini, DISI Genova
"Keep up the great work in this important new area! Congratulations."
A. Richard Newton, Dean and the Roy W. Carson Professor in Engineering,
University of California, Berkeley.
"I'm having fun seeing what happens as the various parameters
are varied...quite a show..."
Grace Wahba, University of Wisconsin-Madison
The Applet has been very successful. It has been downloaded
to date by around 700 individuals affiliated with more than 400
separate companies and institutions in approximately 50 countries
across the world (you can see a list of these companies and institutions
here) and accessed by many more. The feedback
we have received on the performance of the Applet has been universally