Experimentally optimal ν in support vector regression for different noise models and parameter settings-Reference-Cited by-同舟云学术

Experimentally optimal ν in support vector regression for different noise models and parameter settings

Published:2004-01 Issue:1 Volume:17 Page:127-141
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Chalimourda Athanassia,Schölkopf Bernhard,Smola Alex J

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference11 articles.

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3. Duan, K., Keerthi, S. & Poo, A (2001). Evaluation of simple performance measures for tuning SVM hyperparameters (Tech. Rep. No. Control Division Technical Report CD-01-11). Department of Mechanical Engineering, National University of Singapore.

4. Support vector machines for dynamic reconstruction of a chaotic system;Mattera,1999

5. Network information criterion—Determining the number of hidden units for artificial neural networks;Murata;IEEE Transactions on Neural Networks,1994

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