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 …

An increasing amount of unlabeled time series data available render the semi-supervised
paradigm a suitable approach to tackle classification problems with a reduced quantity of …

Urban pollution is usually monitored via fixed stations that provide detailed and reliable
information, thanks to equipment quality and effective measuring protocols, but these …

Many real-world applications, such as those related to sensors, allow collecting large
amounts of inexpensive unlabeled sequential data. However, the use of supervised …

Graph transduction refers to a family of algorithms that learn from both labeled and
unlabeled examples using a weighted graph and scarce label information via regularization …

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 …

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 …