7:30 V. Vapnik, AT& T Labs - Research
“The SVM method of function approximation”
8:00 G. Wahba, University of Wisconsin-Madison
“Reproducing Kernel Hilbert Spaces, Smoothing Spline ANOVA spaces, Support Vector Machines, and all that”
8:30 L. Kaufman, Bell Laboratories, Lucent Technologies
“Solving the Quadratic Programming problem arising in support vector classification”
8:45 C. Burges, Bell Laboratories, Lucent Technologies
“Geometry of Support Vector Machines”
9:00 B. Schölkopf, Max-Planck-Inst. Tübingen and GMD Berlin
“Kernel Principal Component Analysis”
9:15 D. Mattera and S. Haykin, McMaster University
“Support Vector Machines for Dynamic Reconstruction of Chaotic Processes”
9:40 K. Müller, GMD First, Berlin
“Predicting Time Series with Support Vector Machines”
10:00 A. Oren and D. Roth, University of Illinois, Urbana/Champaign
“Support Vectors in Natural Language Disambiguation Tasks”
10:15 T. Joachims, Universität Dortmund
“Support Vector Machines for Text Categorization”
10:30 skiing
16:00 F. Girosi, MIT, Cambridge
“Support Vector Machines, Regularization Theory and Sparse Approximation”
16:25 K. Bennett, Rensselaer Polytechnic Institute
“Combining Support Vector and Mathematical Programming Models for Induction”
16:50 M. Stitson, Royal Holloway London
“Support Vector ANOVA Decomposition”
17:05 J. Weston, Royal Holloway London
“Density Estimation using Support Vector Machines”
17:20 A. Smola, GMD Berlin
“General Cost Functions for Support Vector Regression”
17:35 Y. Freund, AT& T Labs - Research
“Adaboost as a procedure for margin maximization”
18:00 J. Shawe-Taylor, Royal Holloway London
“Data-sensitive PAC Analysis for SV and Other Machines”
18:15 N. Cristianini, University of Bristol
“Bayesian Voting Schemes and Large Margin Algorithms”
18:30 D. Schuurmans, U. Pennsylvania & NEC Research
“Improving and generalizing the basic maximum margin algorithm”
18:45 open discussion
19:00 end