Feedforward neural networks are neural networks with (possibly) multiple layers of neurons
such that each layer is fully connected to the next one. They have been widely studied in the …

Graph-based semi-supervised learning (SSL) algorithms have gained increased attention in
the last few years due to their high classification performance on many application domains …

Semi-supervised classification methods are specialized to use a very limited amount of
labeled data for training and ultimately for assigning labels to the vast majority of unlabeled …

Graph-based semi-supervised learning (SSL) algorithms perform well when the data lie on a
low-dimensional manifold. Although these methods achieved satisfactory performance on a …

Laplacian regularized least squares (LapRLS) is a popular and effective unconstrained
method for semi-supervised learning (SSL). However, many unconstrained methods may be …

Graph-based semi-supervised learning (SSL) algorithms learn through a weighted graph
generated from both labeled and unlabeled examples. Despite the effectiveness of these …

Semi-supervised classification methods are specialized to use a very limited amount of
labelled data for training and ultimately for assigning labels to the vast majority of unlabelled …

One of the widely used algorithms for graph-based semi-supervised learning (SSL) is the
Local and Global Consistency (LGC). Such an algorithm can be viewed as a convex …

Graph-based semi-supervised learning (SSL) methods are effective on many application
domains. Despite such an effectiveness, many of these methods are transductive in nature …

Time series transductive classification on graphs: supplementary material Page 1 Time series
transductive classification on graphs: supplementary material Celso AR Sousa, Vinıcius MA …