Fast Training of Support Vector Machines for Regression

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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	D. Anguita, A. Boni, S. Pace, "Fast Training of Support Vector Machines for Regression," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 5, pp. 5210, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.861459, author = {D. Anguita and A. Boni and S. Pace}, title = {Fast Training of Support Vector Machines for Regression}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {5}, year = {2000}, issn = {1098-7576}, pages = {5210}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861459}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Fast Training of Support Vector Machines for Regression SN - 1098-7576 SP EP A1 - D. Anguita, A1 - A. Boni, A1 - S. Pace, PY - 2000 VL - 5 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

D. Anguita, University of Genova

A. Boni, University of Genova

S. Pace, University of Genova

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861459

We propose here a fast way to perform the gradient computation in Support Vector Machine (SVM) learning, when samples are positioned on an m -dimensional grid. Our method takes advantage of the particular structure of the constrained quadratic programming problem arising in this case. We show how such structure is connected to the properties of block Toeplitz matrices and how they can be used to speed-up the computation of matrix-vector products.

Citation:

D. Anguita, A. Boni, S. Pace, "Fast Training of Support Vector Machines for Regression," ijcnn, vol. 5, pp.5210, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000

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