---
title: "Semi-Supervised Learning from a Translation Model between Data Distributions"
authors:
  - "Henry Anaya-Sánchez"
  - "José Martínez Sotoca"
  - "Adolfo Martínez Usó"
doi: "10.5591/978-1-57735-516-8/IJCAI11-199"
url: "https://ijcai.org/proceedings/2011/199"
pdf: "https://ijcai.org/proceedings/2011/0199.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1165-1169"
track: ""
keywords:
---

# Semi-Supervised Learning from a Translation Model between Data Distributions

Henry Anaya-Sánchez, José Martínez Sotoca, Adolfo Martínez Usó

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1165-1169. DOI: [10.5591/978-1-57735-516-8/IJCAI11-199](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-199)

## 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.