Symbolic learning is the subfield of machine learning concerned with learning predictive
models with knowledge represented in logical form, such as decision tree and decision list …

The current energy scenario requires that gas turbines (GTs) operate at their maximum
efficiency and highest reliability. Trip is one of the most disrupting events that reduces GT …

The current energy market requires that gas turbines (GTs) run efficiently and reliably, thus
improving their sustainability. To this aim, condition monitoring is fundamental for GT …

As well-known, gas turbine (GT) trip causes a reduction of GT lifespan and makes costs
increase, because of unscheduled stops. Thus, predicting GT trip in advance would allow …

One of the most disrupting events that reduces gas turbine (GT) availability and also
significantly increases maintenance costs is trip. Given its nature, data-driven …

From the intricate circuits of digital devices to the abstract realms of logical theory, formula
minimization remains a cornerstone challenge with various implications. This paper …

Multivariate time series classification is an ubiquitous and widely studied problem. Due to
their strong generalization capability, neural networks are suitable for this problem, but their …

Il Symbolic Learning (SL) studia algoritmi di apprendimento per modelli computazionali che
si basano sulla logica formale (o logica simbolica) e, come tale, fornisce modelli di …

Multivariate time series are a very common non-tabular type of data. In many practical cases,
multivariate time series encode real-world situations that include temporal information, and …

Traditional symbolic learning is the sub-field of machine learning that aims to learn symbolic
models from structured data, representing propositional logic theories, and its investigation …