title: Training SVMs with EDR algorithm.
abstract: The aim of this work is to present a new training algorithm for SVMs based on the pattern selection strategy called Error Dependent Repetition (EDR). With EDR, the presentation frequency of a pattern depends on its error: patterns with larger errors are selected more frequently and patterns with smaller error (or learned) are presented with minor frequency. Using a simple iterative process based on gradient ascent, SVM-EDR can solve the dual problem without any assumption about support vectors or the Karush-Kuhn-Tucker (KKT) conditions.
authors.affiliation: Department of Electronics Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. barros@cpdee.ufmg.br
authors.forename: M B
authors.initials: MB
authors.lastname: Almeida
authors.forename: A P
authors.initials: AP
authors.lastname: Braga
authors.forename: J P
authors.initials: JP
authors.lastname: Braga
mesh_terms: D000465:Algorithms; D010363:Pattern Recognition, Automated; D012107:Research Design