Transductive Inference for Estimating Values of Functions
Abstract
We introduce an algorithm for estimating the values of a function at a set of test points Xe+!, ... , xl+m given a set of training points (XI,YI), ... ,(xe,Ye) without estimating (as an intermediate step) the regression function . We demonstrate that this direct (transduc(cid:173) ti ve) way for estimating values of the regression (or classification in pattern recognition) can be more accurate than the tradition(cid:173) alone based on two steps, first estimating the function and then calculating the values of this function at the points of interest.
Cite
Text
Chapelle et al. "Transductive Inference for Estimating Values of Functions." Neural Information Processing Systems, 1999.Markdown
[Chapelle et al. "Transductive Inference for Estimating Values of Functions." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/chapelle1999neurips-transductive/)BibTeX
@inproceedings{chapelle1999neurips-transductive,
title = {{Transductive Inference for Estimating Values of Functions}},
author = {Chapelle, Olivier and Vapnik, Vladimir and Weston, Jason},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {421-427},
url = {https://mlanthology.org/neurips/1999/chapelle1999neurips-transductive/}
}