Support Vectors Selection by Linear Programming

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http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861456

IEEE-INNS-ENNS International Joint Co ...

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	Vojislav Kecman, Ivana Hadzic, "Support Vectors Selection by Linear Programming," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 5, pp. 5193, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.861456, author = {Vojislav Kecman and Ivana Hadzic}, title = {Support Vectors Selection by Linear Programming}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {5}, year = {2000}, issn = {1098-7576}, pages = {5193}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861456}, 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 - Support Vectors Selection by Linear Programming SN - 1098-7576 SP EP A1 - Vojislav Kecman, A1 - Ivana Hadzic, PY - 2000 KW - support vector machines KW - neural networks KW - linear programming KW - learning from experimental data KW - non-linear multivariate function approximations KW - nonlinear multivariate pattern recognition VL - 5 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Vojislav Kecman, University of Auckland

Ivana Hadzic, University of Auckland

A linear programming (LP) based method is proposed for learning from experimental data in solving the nonlinear regression and classification problems. LP controls both the number of basis functions in a neural network (i.e., support vector machine) and the accuracy of learning machine. Two different methods are suggested in regression and their equivalence is discussed. Examples of function approximation and classification (pattern recognition) illustrate the efficiency of the proposed method.

Index Terms:

support vector machines, neural networks, linear programming, learning from experimental data, non-linear multivariate function approximations, nonlinear multivariate pattern recognition

Citation:

Vojislav Kecman, Ivana Hadzic, "Support Vectors Selection by Linear Programming," ijcnn, vol. 5, pp.5193, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000

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