Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments

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http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861457

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	Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola, "Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 5, pp. 5199, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000.

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	@article{ 10.1109/IJCNN.2000.861457, author = {Athanassia Chalimourda and Bernhard Schölkopf and Alex J. Smola}, title = {Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {5}, year = {2000}, issn = {1098-7576}, pages = {5199}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861457}, 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 - Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments SN - 1098-7576 SP EP A1 - Athanassia Chalimourda, A1 - Bernhard Schölkopf, A1 - Alex J. Smola, PY - 2000 VL - 5 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Athanassia Chalimourda, Ruhr-Universit?t Bochum

Bernhard Schölkopf, Microsoft Research

Alex J. Smola, Australian National University

In Support Vector (SV) regression, a parameter \math controls the number of Support Vectors and the number of points that come to lie outside of the so-called \math-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of \math that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression.

Citation:

Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola, "Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments," ijcnn, vol. 5, pp.5199, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000

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