Call for NIPS 2008 Kernel Learning Workshop Submissions
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1998 NIPS Workshop Proceedings available for download
The book "Advances in Large Margin Classifiers", published by MIT Press in 2000 (editors A. Smola, P. Bartlett, B. Schölkopf, D. Schuurmans), is now available as a PDF file from http://www.kernel-machines.org/nips98/book.html. The contents include some of the most cited papers in the field:
Dynamic Alignment Kernels - Chris Watkins
Natural Regularization from Generative Models - Nuria Oliver, Bernhard
Schölkopf, and Alexander J. Smola
Probabilities for SV Machines - John C. Platt
Maximal Margin Perceptron - Adam Kowalczyk
Large Margin Rank Boundaries for Ordinal Regression - Ralf Herbrich, Thore
Graepel, and Klaus Obermayer
Generalized Support Vector Machines - Olvi L. Mangasarian
Linear Discriminant and Support Vector Classifiers - Isabelle Guyon and David
G. Stork
Regularization Networks and Support Vector Machines - Theodoros Evgeniou,
Massimiliano Pontil, and Tomaso Poggio
Robust Ensemble Learning - Gunnar Rätsch, Bernhard Schölkopf, Alexander J.
Smola, Sebastian Mika, Takashi Onoda, and Klaus-Robert Müller
Functional Gradient Techniques for Combining Hypotheses - Llew Mason, Jonathan
Baxter, Peter L. Bartlett, and Marcus Frean
Towards a Strategy for Boosting Regressors - Grigoris Karakoulas and John
Shawe-Taylor
Bounds on Error Expectation for SVM - Vladimir Vapnik and Olivier Chapelle
Adaptive Margin Support Vector Machines - Jason Weston and Ralf Herbrich
GACV for Support Vector Machines - Grace Wahba, Yi Lin, and Hao Zhang
Gaussian Processes and SVM: Mean Field and Leave-One-Out - Manfred Opper and
Ole Winther
Computing the Bayes Kernel Classifier - Pál Ruján and Mario Marchand
Margin Distribution and Soft Margin - John Shawe-Taylor and Nello Cristianini
Support Vectors and Statistical Mechanics - Rainer Dietrich, Manfred Opper,
and Haim Sompolinsky
Entropy Numbers for Convex Combinations and MLPs - Alexander J. Smola, André
Elisseef, Bernhard Schölkopf, and Robert C. Williamson