In the field of machine learning, vector quantization is a category of low-complexity
approaches that are nonetheless powerful for data representation and clustering or …

Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong
to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and …

Abstract Machine learning on trees has been mostly focused on trees as input. Much less
research has investigated trees as output, which has many applications, such as molecule …

Machine Learning (ML) is becoming a more and more popular field of knowledge, being a
term known not only in the academic field due to its successful applications to many real …

Interpretable machine learning classifiers like prototype based models are a promising
alternative to deep neural networks and regarding efforts to make them explainable [1, 9] …

Artificial Intelligence (AI), a discipline with decades of history, is living its golden era due to
striking developments that solve problems that were unthinkable just a few years ago, like …

In this contribution we try to introduce a classification model with rejection as an option,
inspired by RSLVQ [1] and GLVQ [2]. As a matter of simplification, in the beginning we …