Semi-Supervised Gaussian Process Classifiers
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1059-1063.
Abstract
In this paper, we propose a graph-based construction of semi-supervised Gaussian process classifiers. Our method is based on recently proposed techniques for incorporating the geometric properties of unlabeled data within globally defined kernel functions. The full machinery for standard supervised Gaussian process inference is brought to bear on the problem of learning from labeled and unlabeled data. This approach provides a natural probabilistic extension to unseen test examples. We employ Expectation Propagation procedures for evidence-based model selection. In the presence of few labeled examples, this approach is found to significantly outperform cross-validation techniques. We present empirical results demonstrating the strengths of our approach.
@inproceedings{ijcai2007p171,
title = {Semi-Supervised Gaussian Process Classifiers},
author = {Sindhwani, Vikas and Chu, Wei and Keerthi, S. Sathiya},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1059--1063},
year = {2007},
url = {https://ijcai.org/proceedings/2007/171},
}
TY - CPAPER TI - Semi-Supervised Gaussian Process Classifiers AU - Sindhwani, Vikas AU - Chu, Wei AU - Keerthi, S. Sathiya T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1059 EP - 1063 UR - https://ijcai.org/proceedings/2007/171 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
