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2003 | OriginalPaper | Chapter

Multiplicative Updates for Large Margin Classifiers

Authors: Fei Sha, Lawrence K. Saul, Daniel D. Lee

Published in: Learning Theory and Kernel Machines

Publisher: Springer Berlin Heidelberg

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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.

Metadata
Title
Multiplicative Updates for Large Margin Classifiers
Authors
Fei Sha
Lawrence K. Saul
Daniel D. Lee
Copyright Year
2003
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-540-45167-9_15

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