Room 333 Wisenbaker (fishbowl)
Support Vector Machines, an outgrowth of statistical learning theory, constitute a major advance in the field of supervised classification. I will provide an account of the field including aspects of maximum margin classification, soft margins, kernels and the quantification of generalizationerror. Based on (among others):
Boser, Bernhard E., Isabelle M. Guyon, and Vladimir N. Vapnik. “A training algorithm for optimal margin classifiers.” In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144-152. ACM, 1992.
Cortes, Corinna, and Vladimir Vapnik. “Support-vector networks.” Machine Learning, 20, no. 3 (1995): 273-297.
[Recipient of the Paris Kanellakis Theory and Practice Award of ACM for
“revolutionary development of a highly effective algorithm known as Support Vector Machines (SVM), a set of related supervised learning methods used for data classification and regression”, which is “one of the most frequently used algorithms in machine learning, and is used in medical diagnosis, weather forecasting, and intrusion detection amongmany other practical applications”.