ABSTRACT: Support vector regression (SVR) and computational fluid dynamics (CFD) techniques are applied to predict the performance of an automotive torque converter in the design process of turbine ...
Objective: To explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an improved machine learning model. Methods: This ...
library(mlr3) library(mlr3learners) library(mlr3pipelines) library(mlr3tuning) library(paradox) # Establish task task = as_task_classif(tsk("pima")$data(), target ...
ABSTRACT: In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps ...
With a considerable gap between the training and validation accuracy of the proposed GRU+SVM architecture, I posit that the hyperparameters used were sub-optimal. Otherwise, it might have something to ...
In machine learning, algorithms harness the power to unearth hidden insights and predictions from within data. Central to the effectiveness of these algorithms are hyperparameters, which can be ...
Abstract: Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, ...
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