We consider semi-supervised learning, learning task from both labeled and unlabeled
instances and in particular, self-training with decision tree learners as base learners. We …

At present, graph regularized semi-supervised methods achieve excellent performance in
various fields. However, the manifold regularization term of most methods only considers the …

Air pollution has adverse effects on humans and ecosystem, and spatially fine-grained air
quality information (ie, the air quality information of every fine-grained area) can help people …

The present paper focuses on semi-supervised classification problems. Semi-supervised
learning is a learning task through both labeled and unlabeled samples. One of the main …

Inthis article, wefocus on the classification problem to semi-supervised learning in non-
stationary environment. Semi-supervised learning is a learning task from both labeled and …

Bioinformatics research is regarded as an area which encompasses voluminous, expanding
and complex datasets. Nowadays, with the use of high-throughput next-generation …

In this article, we focus on the multiclass classification problem to semi-supervised learning.
Semi-supervised learning is a learning task from both labeled and unlabeled data points …

This works investigates the use of margin and diversity, two key concepts in ensemble
learning, to develop a versatile uncertainty-driven ensemble classifier, under the scarcity of …

In this article we focus on the semi-supervised learning. Semi-supervised learning typically
is a learning task from both labeled and unlabeled data. We especially consider the …

Currently, most graph regularization algorithms including LapSVM and LapPPSVM utilize
unlabeled data for semi-supervised learning by introducing manifold regularization term …