New Preprint Server (SVMs, COLT, etc)

[I C G Campbell]

NEW PREPRINT SERVER

We have started up a new preprint server at:

http://lara.enm.bris.ac.uk/cig/pubs_nf.htm

This contains 16 recent preprints with the titles and
authorship listed below. The server would mainly be of interest
to researchers in the area of support vector machines
and computational learning theory. There are also some
further papers on the application of machine learning
techniques to medical decision support.

Enjoy!

Colin Campbell (Bristol University)
_________________________________

1. Data Dependent Structural Risk Minimization for Perceptron
Decision Tress. John Shawe-Taylor and Nello Cristianini.

2. Bayesian Voting Schemes and Large Margin Classifiers. 
Nello Cristianini and John Shawe-Taylor.

3. Bayesian Classifiers are Large Margin Hyperplanes in a
Hilbert Space. Nello Cristainini, John Shawe-Taylor
and Peter Sybacek, in Shavlik J. (editor)

4. Bayesian Classifiers are Large Margin Hyperplanes in a
Hilbert Space. Nello Cristianini, John Shawe-Taylor and Peter 
Sykacek.  

5. Margin Distribution Bounds on Generalisation. John Shawe-Taylor
and Nello Cristianini.

6. Robust Bounds on Generalisation from the Margin Distribution.
John Shawe-Taylor and Nello Cristianini.  

7. Simple Training Algorithms for Support Vector Machines.
Colin Campbell and Nello Cristianini.

8. The Kernel-Adatron: a Fast and Simple Learning Procedure for 
Support Vector Machines. Thilo Friess, Nello Cristianini and 
Colin Campbell.  

9. Large Margin Classification Using the Kernel Adatron Algorithm.
Colin Campbell, Thilo Friess and Nello Cristianini.

10. Dynamically Adapting Kernels in Support Vector Machines. 
Nello Cristianini, Colin Campbell and John Shawe-Taylor.

11. Multiplicative Updatings for Support Vector Machines.
Nello Cristianini, Colin Campbell and John Shawe-Taylor.     

12. Enlarging the Margin in Perceptron Decision Trees. Kristin
Bennett, Nello Critianini, John Shawe-Taylor and Donghui Wu. 

13. Large Margin Decision Trees for Induction and Transduction.
Donghui Wu, Kristin Bennett, Nello Cristianini and John Shawe-Taylor.

14. Bayes Point Machines: Estimating the Bayes Point in Kernel Space.
Ralf Herbrich, Thore Graepel and Colin Campbell.

15. Bayesian Learning in Reproducing Kernel Hilbert Spaces: 
The Usefulness of the Bayes Point. Ralf Herbrich, Thore Graepel and 
Colin Campbell. 

16. Further Results on the Margin Distribution. John Shawe-Taylor
and Nello Cristianini.