Schedule
12/4/1998 – Morning Session
Chair: Dale Schuurmans
- 07:30-07:55 Leo Breiman
- 07:55-08:15 Yoram Singer
- 08:15-08:35 Florence d’Alche-Buc
- 08:35-08:50 Grigoris Karakoulas
- 08:50-09:05 Coffee Break
- 09:05-09:20 Bernhard Schölkopf
- 09:20-09:40 Takashi Onoda
- 09:40-09:55 John Baxter
- 09:55-10:10 Peter Bartlett
- 10:10-10:30 Nello Cristianini
12/4/1998 – Afternoon Session
Chair: Peter Bartlett
- 16:00-16:20 Dale Schuurmans
- 16:20-16:40 Manfred Warmuth
- 16:40-16:55 Yoav Freund
- 16:55-17:10 Thilo Frieß
- 17:10-17:25 Qun Zhao
- 17:25-17:40 Pal Rujan
- 17:40-17:45 Coffee Break
- 17:45-19:00 Organized Panel Discussion
12/5/1998 – Morning Session
Chair: Alex Smola
- 07:30-08:30 Tutorial on Mathematical Programming – Olvi Mangasarian
- 08:30-08:50 Kristin Bennett
- 08:50-09:05 Coffee Break
- 09:05-09:25 Ralf Herbrich
- 09:25-09:45 David Stork
- 09:45-10:00 Chris Burges
- 10:00-10:15 Grace Wahba
- 10:15-10:30 Joachim Buhmann
12/5/1998 – Afternoon Session
Chair: Bernhard Schökopf
- 16:00-16:25 Vladimir Vapnik
- 16:25-16:40 Jason Weston
- 16:40-17:00 Leonid Gurvits
- 17:00-17:25 Alex Smola
- 17:25-17:40 Coffee Break
- 17:40-17:55 Manfred Opper
- 17:55-18:10 Ole Winter
- 18:10-18:25 Adam Kowalzyk
- 18:25-18:40 John Platt
- 18:40-19:00 Wrap Up Session
Speakers
- Florence d’Alche-Buc, Laboratoire d’Informatique de Paris 6, Paris
Estimated margin data for Boosting and SVM - Peter Bartlett, Australian National University
Error Estimates for Large Margin Classifiers - Jonathan Baxter, Australian National University
Nearest neighbour classification and large margins - Kristin Bennett, Rensslaer Polytechnic Institute
Enlarging the Margins in Perceptron Decision Trees - Leo Breiman, Stanford University
Are margins relevant in voting? - Joachim Buhmann, Universität Bonn
Empirical Risk Approximation for Unsupervised Learning - Chris Burges, Lucent Technologies
Discriminative Gaussian Mixture Models - Nello Cristianini, University of Bristol
Margin Distribution Bounds on Generalization - Yoav Freund, AT&T Research
Large Margin Classification Using the Perceptron Algorithm - Thilo Frieß, GMD Berlin
Support Vector Neural Networks - Leonid Gurvits, NEC Research
A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces - David Stork, CRC Ricoh, Isabelle Guyon, Clopinet
What linear discriminant techniques teach us about SVMs - Ralf Herbrich, TU Berlin
Large Margin Rank Boundaries for Preference Learning - Grigoris Karakoulas, Global Analytics, CIBC
Boosting Regressors - Adam Kowalczyk, Trevor Anderson, Telstra Research Laboratories
Local training of support vector machines - Olvi Mangasarian, University of Madison
Mathematical Programming in Machine Learning - Takashi Onoda, CRIEPI, Gunnar Rätsch, GMD FIRST
-AdaBoost Classification - Manfred Opper, NCRG, Aston University
Statistical Mechanics of Support Vector Learning - John Platt, Microsoft Research
Making Support Vector Machines Yield Probabilities - Pal Rujan, University of Oldenburg
Computing the Bayes Support Vector Machine - Bernhard Schölkopf, GMD FIRST
Reparametrizing SV Machines - Dale Schuurmans, University of Waterloo
General linear ensemble methods for learning - Yoram Singer, AT&T Research
Boosted Vector Machines - Alex Smola, GMD FIRST
Entropy Numbers, Operators, and Kernels - Vladimir Vapnik, AT&T Research
Error Bounds for Support Vector Machines - Grace Wahba, University of Madison
A look at Structural Risk Minimization, Generalized Approximate Cross Validation, and the Generalized Degrees of Freedom for Signal - Manfred Warmuth, UC Santa Cruz
Hinge Loss and Average Margins for Predicting with Linear Threshold Functions with Applications to Boosting - Jason Weston, Royal Holloway, London
Predictive power of bounds and stacking support vector machines - Ole Winther, Theoretical Physics, University of Lund
Mean Field Algorithms for Gaussian Process Classification and Support Vector Machines - Qun Zhao, University of Florida
Automatic Target Recognition with Support Vector Machines