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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5
Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
ASCII Text
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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.
 
 
BibTex
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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
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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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