Quantum machine learning can play an essential role in smart manufacturing applications.
This paper aimed to understand the state of the art of quantum computing in machine …

Over the last few decades, quantum machine learning has emerged as a groundbreaking
discipline. Harnessing the peculiarities of quantum computation for machine learning tasks …

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 …

Quantum machine learning aims to execute machine learning algorithms in quantum
computers by utilizing powerful laws like superposition and entanglement for solving …

We present an approach to discriminate SARS-CoV-2 virus types based on their RNA
sequence descriptions avoiding a sequence alignment. For that purpose, sequences are …

Abstract Machine learning (ML) has emerged into formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

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 …

This paper introduces concepts for quantum computing based learning vector quantization
and prototype learning models. These concepts relate to current quantum computing …

The vibrant Mediterranean city of Barcelona, in Spain, welcomed the 13th edition of WSOM+
2019, the Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering …

The leading cause of death worldwide is heart disease. An effective hybrid classifier model
is finally constructed to classify records and produce predictions or identifications based on …