@inProceedings{
 title = {A study on the differences in the interpolation capabilities of models.},
 type = {inProceedings},
 year = {2005},
 pages = {202-207},
 publisher = {Proc. IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications (SMCia/05), Espoo, Finland},
 id = {1e03e16b-f706-343a-9901-b1b409383df9},
 created = {2019-11-19T13:00:51.293Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:46:24.922Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {isg:626},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {We examined the interpolation capabilities of learning methods using simulated data sets and a real data set. We compared five common learning methods for their generalisation capability on the boundaries of the training data set. Also, we examined the effects of the complexity of models on interpolation capability. Our main results were that there are differences between the different model families, but model complexity does not have a major effect on interpolation capability. The multi-layer perceptron, support vector regression and additive spline models outperformed local linear regression and quadratic regression in interpolation capabilities. Information about the interpolation capability of models is useful when, for example, evaluating the reliability of prediction.},
 bibtype = {inProceedings},
 author = {Juutilainen I, Röning J & Laurinen P}
}

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