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