GPDT is a C++ software designed to train large-scale Support Vector Machines (SVMs) for binary classification in both scalar and distributed memory parallel environments [1,3,5,6]. It uses a popular problem decomposition technique to split the SVM quadratic programming (QP) problem into a sequence of smaller QP subproblems, each one being solved by a suitable gradient projection method (GPM). The currently implemented GPMs are the Generalized Variable Projection Method (GVPM) [2] and the Dai-Fletcher method (DFGPM) [4]. [1] G. Zanghirati, L. Zanni, A Parallel Solver for Large Quadratic Programs in Training Support Vector Machines, Parallel Computing 29 (2003), 535-551.[2] T. Serafini, G. Zanghirati, L. Zanni, Gradient Projection Methods for Large Quadratic Programs and Applications in Training Support Vector Machines, Optim. Meth. Soft. 20 (2005), 353-378.[3] T. Serafini, L. Zanni, On the Working Set Selection in Gradient Projection-based Decomposition Techniques for Support Vector Machines, Optim. Meth. Soft. 20 (2005), 583-596.[4] Y.H. Dai, R. Fletcher, New Algorithms for Singly Linearly Constrained Quadratic Programming Problems Subject to Lower and Upper Bounds, Math. Prog. 106(3) (2006), 403-421. [5] L. Zanni, An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines, Computational Management Science 3(2) (2006), 131-145.[6] L. Zanni, T. Serafini, G. Zanghirati, Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems, JMLR 7(Jul), 1467-1492, 2006.

PGPDT: Parallel Gradient Projection-based Decomposition Technique / T., Serafini; Zanni, Luca; G., Zanghirati. - ELETTRONICO. - (2007).

PGPDT: Parallel Gradient Projection-based Decomposition Technique

ZANNI, Luca;
2007

Abstract

GPDT is a C++ software designed to train large-scale Support Vector Machines (SVMs) for binary classification in both scalar and distributed memory parallel environments [1,3,5,6]. It uses a popular problem decomposition technique to split the SVM quadratic programming (QP) problem into a sequence of smaller QP subproblems, each one being solved by a suitable gradient projection method (GPM). The currently implemented GPMs are the Generalized Variable Projection Method (GVPM) [2] and the Dai-Fletcher method (DFGPM) [4]. [1] G. Zanghirati, L. Zanni, A Parallel Solver for Large Quadratic Programs in Training Support Vector Machines, Parallel Computing 29 (2003), 535-551.[2] T. Serafini, G. Zanghirati, L. Zanni, Gradient Projection Methods for Large Quadratic Programs and Applications in Training Support Vector Machines, Optim. Meth. Soft. 20 (2005), 353-378.[3] T. Serafini, L. Zanni, On the Working Set Selection in Gradient Projection-based Decomposition Techniques for Support Vector Machines, Optim. Meth. Soft. 20 (2005), 583-596.[4] Y.H. Dai, R. Fletcher, New Algorithms for Singly Linearly Constrained Quadratic Programming Problems Subject to Lower and Upper Bounds, Math. Prog. 106(3) (2006), 403-421. [5] L. Zanni, An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines, Computational Management Science 3(2) (2006), 131-145.[6] L. Zanni, T. Serafini, G. Zanghirati, Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems, JMLR 7(Jul), 1467-1492, 2006.
2007
T., Serafini; Zanni, Luca; G., Zanghirati
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/641762
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