This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian process to ...

Abstract. This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian ...

This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian process to ...

PDF | This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian.

This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian process to ...

(a) The harmonic func- tion algorithm significantly outperforms the linear kernel SVM, demonstrating that the semi-supervised learning algorithm successfully.

We propose a data-efficient Gaussian process-based Bayesian approach to the semi- supervised learning problem on graphs. The proposed model shows extremely.

Feb 26, 2021 · Abstract—Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data.

Graph Based Multi-Class Semi-Supervised Learning Using Gaussian Process by Yangqiu Song, Changshui Zhang, Jianguo Lee published in Lecture Notes in.

This repository contains graph-based semi-supervised learning (GSSL) papers mentioned in our GSSL survey. We will update this paper list to include new GSSL ...