With their introduction in 1995, Support Vector Machines (SVMs) marked
the beginningofanewerainthelearningfromexamplesparadigm.Rootedinthe
Statistical Learning Theory developed by Vladimir Vapnik at AT&T, SVMs
quickly gained attention from the pattern recognition community due to a
n- beroftheoreticalandcomputationalmerits.Theseinclude, forexample, the
simple geometrical interpretation of the margin, uniqueness of the
solution, s- tistical robustness of the loss function, modularity of the
kernel function, and over't control through the choice of a single
regularization parameter. Like all really good and far reaching ideas,
SVMs raised a number of -
terestingproblemsforboththeoreticiansandpractitioners.Newapproachesto
Statistical Learning Theory are under development and new and more
e?cient methods for computing SVM with a large number of examples are
being studied. Being interested in the development of trainable systems
ourselves, we decided to organize an international workshop as a
satellite event of the 16th Inter- tional Conference on Pattern
Recognition emphasizing the practical impact and relevance of SVMs for
pattern recognition. By March 2002, a total of 57 full papers had been
submitted from 21 co-
tries.Toensurethehighqualityofworkshopandproceedings, theprogramc-
mitteeselectedandaccepted30ofthemafterathoroughreviewprocess.Ofthese
papers16werepresentedin4oralsessionsand14inapostersession.Thepapers span
a variety of topics in pattern recognition with SVMs from computational
theoriestotheirimplementations.Inadditiontotheseexcellentpresentations,
there were two invited papers by Sayan Mukherjee, MIT and Yoshua Bengio,
University of Montreal.