Researchers explore quantum machine learning to detect financial risk faster in high-frequency trading, achieving promising accuracy in experimental models.

The authors investigate a quantum support vector algorithm that uses qudits to identify the most accurate way of solving a prototype machine learning task: the binary classification of point clusters.

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Support vector machines are recognized as a powerful tool for supervised analysis and classification in different fields, particularly geophysics. In summary, SVMs are binary classifiers. Thus, for ...

Henry Krumb School of Mines, Earth and Environmental Engineering Department, Columbia University, New York, New York 10027, United States ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The aim of this study was to analyze the proteomic differences in bone marrow aspirate ...

In the era of big data and artificial intelligence, machine learning is one of the hot issues in the field of credit rating. On the basis of combing the literature on credit rating methods at home and ...

Selecting optimal kernels for regression in physical systems remains a challenge, often relying on trial-and-error with standard functions. In this work, we establish ...