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.

Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging (DTI) to ...

#11 ERROR: process "/bin/sh -c wget https://www.mathworks.com/mpm/glnxa64/mpm && chmod +x mpm && sudo HOME=${HOME} ./mpm install --release=${MATLAB_RELEASE ...

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 ...

Kernel methods and support vector machines (SVMs) serve as cornerstones in modern machine learning, offering robust techniques for both classification and regression tasks. At their core, kernel ...

This project was developed as part of my Master's programm at Heilbronn University. The goal is to classify different oil samples (e.g. olive oil, sunflower oil) based on their fluorescence and ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...

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 ...

Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are ...

The rising demand for high-quality movie recommendations in streaming services necessitates more efficient algorithms capable of handling large datasets. Traditional recommendation systems often ...