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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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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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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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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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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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Efficient nearest neighbors methods for support vector machines in high dimensional feature spaces
In the context of support vector machines, identifying the support vectors is a key issue when dealing with large data sets. In Camelo et al. (Ann...
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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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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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Twofold State Assignment for the Moore Finite State Machines
A method is proposed for reducing the hardware expenditure in the circuits of the Moore finite-state machines (FSMs) implemented in the EMB and LUT...
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Inverse Free Universum Twin Support Vector Machine
Universum twin support vector machine ( \( \mathfrak {U} \)...
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Non-interior-point smoothing Newton method for CP revisited and its application to support vector machines
Non-interior-point smoothing Newton method (SNM) for optimization have been widely studied for over three decades. SNM is a popular approach for...
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Applying an Approximation of the Anderson Discriminant Function and Support Vector Machines for Solving Some Classification Tasks
The Anderson discriminant function has a number of properties useful for solving classification problems and for evaluating posterior class...
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Molecular Fingerprint Based and Machine Learning Driven QSAR for Bioconcentration Pathways Determination
Quantitative structure-activity relationship associates molecules’ structural characteristics to their bio-activity, and it can be performed via...
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Extending electronic medical records vector models with knowledge graphs to improve hospitalization prediction
BackgroundArtificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening...
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Towards a Unified theory of Fractional and Nonlocal Vector Calculus
Nonlocal and fractional-order models capture effects that classical partial differential equations cannot describe; for this reason, they are...
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Coarse geometric kernels for networks embedding
We develop embedding kernels based on the Forman–Ricci curvature and intertwined Bochner–Laplacian and employ them for the detection of the coarse...
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Solving Partial Differential Equations by LS-SVM
In recent years, much attention has been paid to machine learning-based numerical approaches due to their applications in solving difficult...
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A novel method for hierarchical variational inequality with split common fixed point constraint
We address the strongly monotone variational inequality problem over the solution set of the split common fixed point problem with demimetric...
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Support Vector Machines
SVM is one of the most popular nonparametric classification algorithms. It is optimal and is based on computational learning theory. This chapter is...
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Exploring Sign Language Recognition Methods: An Effective Kernel Approach
Universally, sign language is the widely used mode of communication for hearing-impaired people. Several conflicting investigations on recognition...