Pattern Recognition with Support Vector Machines: First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002. Proceedings (Paperback) (ISBN-13: 9783540440161)

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.

Author: Seong-Whan Lee
ISBN-10: 354044016X
ISBN-13: 9783540440161
Publisher: Springer
Language: English
Published: 07/29/2002
Pages: 428
Format: Paperback
Weight: 1.39lbs
Size: 9.30h x 6.22w x 0.68d