Abstract: Machine learning methods are becoming more and more popular in the field of computer-aided drug design. The specific data characteristic, including sparse, binary representation as well as ...

A Support Vector Machine (SVM) is a supervised machine learning model. In its basic form SVMs are used for binary classification tasks. Their fundamental idea is to learn a hyperplane which separates ...

Ischemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients ...

Abstract: With the continuous development of artificial intelligence, machine learning has shown great ability in classification and regression problems. For example, logistic regression, SVM methods, ...

Alternatively, you can run the indiviudal commands in the SQL file to load a particular data set.

ABSTRACT: In machines learning problems, Support Vector Machine is a method of classification. For non-linearly separable data, kernel functions are a basic ingredient in the SVM technic. In this ...

Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored ...

In supervised learning, a set of input variables, such as blood metabolite or gene expression levels, are used to predict a quantitative response variable like hormone level or a qualitative one such ...

In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In ...