Open source software for training large-scale linear and nonlinear Support Vector Machines within scalar environments. It implements a standard decomposition approach for the dual quadratic programming (QP) formulation, where the solver for the inner QP problems is based on iterative gradient-projection methods. An effective caching strategy is also used to save computational time in kernel evaluations. The main feature is that it can efficiently handle medium-to-large inner QP problems, whilst all the other competing packages can not.

GPDT - Gradient-Projection-based Decomposition Technique

ZANGHIRATI, Gaetano;
2004

Abstract

Open source software for training large-scale linear and nonlinear Support Vector Machines within scalar environments. It implements a standard decomposition approach for the dual quadratic programming (QP) formulation, where the solver for the inner QP problems is based on iterative gradient-projection methods. An effective caching strategy is also used to save computational time in kernel evaluations. The main feature is that it can efficiently handle medium-to-large inner QP problems, whilst all the other competing packages can not.
2004
support vector machines; large-scale; classification; machine learning; gradient-projection methods
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/521121
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