Symbolic learning is the logic-based approach to machine learning, and its mission is to
provide algorithms and methodologies to extract logical information from data and express it …

In today's digital information age, human exposure to visual artifacts has reached an
unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the …

Gas turbine trip is an operational event that arises when undesirable operating conditions
are approached or exceeded, and predicting its onset is a largely unexplored area. The …

The study of the electroencephalogram signals recorded from subjects during an experience
is a way to understand the brain processes that underlie their physical and emotional …

Land cover classification (LCC) refers to the task of classifying each pixel in satellite/aerial
imagery by predicting a label carrying information about its nature. Despite the importance of …

Air quality modelling that relates meteorological, car traffic, and pollution data is a
fundamental problem, approached in several different ways in the recent literature. In …

Symbolic learning is the logic-based approach to machine learning. The mission of symbolic
learning is to provide algorithms and methodologies to extract logical information from data …

Multivariate time series classification is a widely known problem, and its applications are
ubiquitous. Due to their strong generalization capability, neural networks have been proven …

Symbolic learning represents the most straightforward approach to interpretable modeling,
but its applications have been hampered by a single structural design choice: the adoption …

Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of
aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic …