Semi-Supervised Learning from a Translation Model between Data Distributions
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1165-1169.
Abstract
In this paper, we introduce a probabilistic classification model to address the task of semi-supervised learning. The major novelty of our proposal stems from measuring distributional relationships between the labeled and unlabeled data. This is achieved from a stochastic translation model between data distributions that is estimated from a mixture model. The proposed classifier is defined from the combination of both the translation model and a kernel logistic regression on labeled data. Experimental results obtained over synthetic and real-world data sets validate the usefulness of our proposal.
@inproceedings{ijcai2011p199,
title = {Semi-Supervised Learning from a Translation Model between Data Distributions},
author = {Anaya-Sánchez, Henry and Sotoca, José Martínez and Usó, Adolfo Martínez},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1165--1169},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-199},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-199},
}
TY - CPAPER TI - Semi-Supervised Learning from a Translation Model between Data Distributions AU - Anaya-Sánchez, Henry AU - Sotoca, José Martínez AU - Usó, Adolfo Martínez T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1165 EP - 1169 DO - 10.5591/978-1-57735-516-8/IJCAI11-199 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-199 ER -Copied
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