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An uncertain support vector machine with imprecise observations
Support vector machines have been widely applied in binary classification, which are constructed based on crisp data. However, the data obtained in...
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Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines
Sparse optimization is about finding minimizers of functions characterized by a number of nonzero components as small as possible, such paradigm...
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A Robust Nonlinear Support Vector Machine Approach for Vehicles Smog Rating Classification
Nowadays all new vehicles are labelled in terms of their emissions thanks to ad hoc legislation. However, from a practical perspective, it is...
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Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines Theory, Algorithms and Applications
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various...
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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification
In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of...
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Fuzzy support vector regressions for short-term load forecasting
The accurate short-term point and probabilistic load forecasts are critically important for efficient operation of power systems and electricity...
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Distributionally robust joint chance-constrained support vector machines
In this paper, we investigate the chance-constrained support vector machine (SVM) problem in which the data points are virtually uncertain although...
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Robust support vector quantile regression with truncated pinball loss (RSVQR)
Support vector quantile regression (SVQR) adapts the flexible pinball loss function for empirical risk in regression problems. Furthermore,
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MRO Inventory Demand Forecast Using Support Vector Machine – A Case Study
Today’s world is living in the age of digital transformation, the so-called Industry 4.0, in which technological advances have revolutionized the...
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Inverse Free Universum Twin Support Vector Machine
Universum twin support vector machine ( \( \mathfrak {U} \)...
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Determination of Probability of Failure of Structures Using DBSCAN and Support Vector Machine
Nowadays, the advanced machine learning method, support vector machine (SVM), is used to determine the probability of failure of a system. The aim of...
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Hyper-Parameter Optimization in Support Vector Machine on Unbalanced Datasets Using Genetic Algorithms
Hyper-parameter optimization and class imbalance are two challenging problems for machine learning in many real-world applications. A hyper-parameter...
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New Interior-Point Approach for One- and Two-Class Linear Support Vector Machines Using Multiple Variable Splitting
Multiple variable splitting is a general technique for decomposing problems by using copies of variables and additional linking constraints that...
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Bilevel hyperparameter optimization for support vector classification: theoretical analysis and a solution method
Support vector classification (SVC) is a classical and well-performed learning method for classification problems. A regularization parameter, which...
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Performance of Genocchi wavelet neural networks and least squares support vector regression for solving different kinds of differential equations
In this study, two numerical methods [(a) artificial neural network method with three layers (input layer, hidden layer, output layer) and (b) least...
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Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries
AbstractThe search for new solid ionic conductors is an important topic of material science that requires significant resources, but can be...
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Machine Learning Based Decision Support System for Resilient Supplier Selection
A supply chain consists of different entities to fulfill a customer’s request. Supplier is one such key entity that plays a pivotal role in the...
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Comparative study of physics-based model and machine learning model for epidemic forecasting and countermeasure
Forecasting the transmission patterns of infectious diseases is of paramount importance in gaining valuable insights into outbreak growth and...
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Numerical simulation of Volterra–Fredholm integral equations using least squares support vector regression
In this paper, a new method based on least squares support vector regression (LS-SVR) is presented as a numerical method for solving linear and...
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A New Insight on Augmented Lagrangian Method with Applications in Machine Learning
By exploiting double-penalty terms for the primal subproblem, we develop a novel relaxed augmented Lagrangian method for solving a family of convex...
