A systematic survey of computer-aided diagnosis in medicine: Past and present developments
J Yanase, E Triantaphyllou - Expert Systems with Applications, 2019 - Elsevier
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort
expended in the interface of medicine and computer science. As some CAD systems in …
expended in the interface of medicine and computer science. As some CAD systems in …
A stable variant of linex loss SVM for handling noise with reduced hyperparameters
Abstract Support Vector Machine (SVM) primarily uses the hinge loss function with maximum
margin. The boundary instances determine the separating hyperplanes. However, in real …
margin. The boundary instances determine the separating hyperplanes. However, in real …
Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine
J Hu, T Zhou, S Ma, D Yang, M Guo, P Huang - Scientific Reports, 2022 - nature.com
The rock mass is one of the key parameters in engineering design. Accurate rock mass
classification is also essential to ensure operational safety. Over the past decades, various …
classification is also essential to ensure operational safety. Over the past decades, various …
Improved pairs trading strategy using two-level reinforcement learning framework
Z Xu, C Luo - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Pairs trading is a popular classic neutral trading strategy in financial market. Deep
reinforcement learning (DRL) has been widely used to improve the performance of this …
reinforcement learning (DRL) has been widely used to improve the performance of this …
Predictive models for bariatric surgery risks with imbalanced medical datasets
Bariatric surgery (BAR) has become a popular treatment for type 2 diabetes mellitus which is
among the most critical obesity-related comorbidities. Patients who have bariatric surgery …
among the most critical obesity-related comorbidities. Patients who have bariatric surgery …
Valley-loss regular simplex support vector machine for robust multiclass classification
L Tang, Y Tian, W Li, PM Pardalos - Knowledge-Based Systems, 2021 - Elsevier
Noise and outlier data processing are important issues to support vector machine (SVM).
Although the pinball-loss SVM (Pin-SVM) and ramp-loss SVM (Ramp-SVM) are able to deal …
Although the pinball-loss SVM (Pin-SVM) and ramp-loss SVM (Ramp-SVM) are able to deal …
Ramp-loss nonparallel support vector regression: robust, sparse and scalable approximation
L Tang, Y Tian, C Yang, PM Pardalos - Knowledge-Based Systems, 2018 - Elsevier
Although the twin support vector regression (TSVR) has been extensively studied and
diverse variants are successfully developed, when it comes to outlier-involved training set …
diverse variants are successfully developed, when it comes to outlier-involved training set …
Support vector machine with eagle loss function
SVM utilizes the hinge loss function and maximum margin to find the separating hyperplane.
In SVM, only the boundary instances/support vectors confine the separating hyperplane …
In SVM, only the boundary instances/support vectors confine the separating hyperplane …
Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future Directions
R Bennett, M Hemmati, R Ramesh… - Dynamics of Disasters …, 2024 - Springer
Conventional medical practices rely on population-derived guidelines, striving for optimal
outcomes for the “average” patient through a so-called “one-size-fits-all” approach. Precision …
outcomes for the “average” patient through a so-called “one-size-fits-all” approach. Precision …
Twin bounded weighted relaxed support vector machines
Data distribution has an important role in classification. The problem of imbalanced data has
occurred when the distribution of one class, which usually attends more interest, is …
occurred when the distribution of one class, which usually attends more interest, is …