Transductive Classification via Local Learning Regularization
Abstract
The idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive Classification (TC) problems. Specifically, we formulate a Local Learning Regularizer (LL-Reg) which leads to a solution with the property that the label of each data point can be well predicted based on its neighbors and their labels. For model selection, an efficient way to compute the leave-one-out classification error is provided for the proposed and related algorithms. Experimental results using several benchmark datasets illustrate the effectiveness of the proposed approach.
Cite
Text
Wu and Schölkopf. "Transductive Classification via Local Learning Regularization." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Wu and Schölkopf. "Transductive Classification via Local Learning Regularization." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/wu2007aistats-transductive/)BibTeX
@inproceedings{wu2007aistats-transductive,
title = {{Transductive Classification via Local Learning Regularization}},
author = {Wu, Mingrui and Schölkopf, Bernhard},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {628-635},
volume = {2},
url = {https://mlanthology.org/aistats/2007/wu2007aistats-transductive/}
}