Departmental Papers (CIS)

Date of this Version

August 2003

Document Type

Conference Paper


Postprint version. Published in Lecture Notes in Computer Science, Volume 2777, Learning Theory and Kernel Machines, 2003, pages 188-202.
Publisher URL:


Various problems in nonnegative quadratic programming arise in the training of large margin classifiers. We derive multiplicative updates for these problems that converge monotonically to the desired solutions for hard and soft margin classifiers. The updates differ strikingly in form from other multiplicative updates used in machine learning. In this paper, we provide complete proofs of convergence for these updates and extend previous work to incorporate sum and box constraints in addition to nonnegativity.



Date Posted: 11 September 2005