Structured Machine Learning for 'Soft' Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing and Evaluation
Abstract
We describe the use of smoothing spline analysis of variance (SS(cid:173) ANOVA) in the penalized log likelihood context, for learning (estimating) the probability p of a '1' outcome, given a train(cid:173) ing set with attribute vectors and outcomes. p is of the form pet) = eJ(t) /(1 + eJ(t)), where, if t is a vector of attributes, f is learned as a sum of smooth functions of one attribute plus a sum of smooth functions of two attributes, etc. The smoothing parameters governing f are obtained by an iterative unbiased risk or iterative GCV method. Confidence intervals for these estimates are available.
Cite
Text
Wahba et al. "Structured Machine Learning for 'Soft' Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing and Evaluation." Neural Information Processing Systems, 1993.Markdown
[Wahba et al. "Structured Machine Learning for 'Soft' Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing and Evaluation." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/wahba1993neurips-structured/)BibTeX
@inproceedings{wahba1993neurips-structured,
title = {{Structured Machine Learning for 'Soft' Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing and Evaluation}},
author = {Wahba, Grace and Wang, Yuedong and Gu, Chong and Md, Ronald Klein and Md, Barbara Klein},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {415-422},
url = {https://mlanthology.org/neurips/1993/wahba1993neurips-structured/}
}