Training a Support Vector Machine in the Primal

Citations Over TimeTop 1% of 2007 papers

Abstract

Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.

Related Papers

• → Classification using support vector machines with graded resolution(2005)31 cited
• → Smoothing Support Vector Machines for e-Insensitive Regressi(2006)2 cited
• On Multiclass Support Vector Machines: One-Against-Half Approach(2010)
• 재무예측을 위한 Support Vector Machine의 최적화(2011)
• [발표논문] 인공신경망과 Support Vector Machine의 기업부도예측 성과 비교-Support Vector Machine의 유용성을 중심으로-(2004)