A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Evaluation of Performance Measures for SVR Hyperparameter Selection
2007
Neural Networks (IJCNN), International Joint Conference on
To obtain accurate modeling results, it is of primal importance to find optimal values for the hyperparameters in the Support Vector Regression (SVR) model. In general, we search for those parameters that minimize an estimate of the generalization error. In this study, we empirically investigate different performance measures found in the literature: k-fold cross-validation, the computationally intensive, but almost unbiased leave-oneout error, its upper bounds -radius/margin and span bound -,
doi:10.1109/ijcnn.2007.4371031
dblp:conf/ijcnn/SmetsVJ07
fatcat:xvg4a7bdurgszdvszpx55nsu7e