Marco Perrotta
Hi, my name is Marco Perrotta. I'm a master student in computer science at the University of Ferrara, where I also work as a collaborator at the Applied Computational Logic and Artificial Intelligence Lab. My main interest is how technology can be used to understand and study language.
Session
Symbolic learning is a branch of machine learning focused on building classifiers that can be translated into logical rules, making them far more readable than neural networks or other statistical models. While training a symbolic model is a necessary first step, it is the post-processing stage that yields the most relevant insights. We present a live walkthrough of SolePostHoc.jl, a SOLE package dedicated to post-processing, allowing for rule extraction, boosting and model simplification.