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Learning via variably scaled kernels
We investigate the use of the so-called variably scaled kernels (VSKs) for learning tasks, with a particular focus on support vector machine (SVM)...
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Efficient Distributed Estimation of High-dimensional Sparse Precision Matrix for Transelliptical Graphical Models
In this paper, distributed estimation of high-dimensional sparse precision matrix is proposed based on the debiased D-trace loss penalized lasso and...
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A multi-objective approach for manufacturing systems with multiple production routes based on supervisory control theory and heuristic algorithms
Heterogeneity among equipment in industrial production lines may have a major impact on energy consumption and makespan. The Supervisory Control...
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Stress Contagion Protocols for Human and Autonomous Robot Teams
The objective of this paper is to formalize the stress contagion protocols in first responder teams which respond to life-threatening crises on a...
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A Survey of Statistical Learning Techniques as Applied to Inexpensive Pediatric Obstructive Sleep Apnea Data
Pediatric obstructive sleep apnea affects an estimated 1–5% of elementary-school aged children and can lead to additional health problems. Swift...
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An Introduction to Sparse Matrices
Consider the simple matrix A on the left in Figure 1.1. Many of its entries are zero (and so are omitted). This is an example of a sparse matrix. The...
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High-multiplicity N-fold IP via configuration LP
N -fold integer programs (IPs) form an important class of block-structured IPs for which increasingly fast algorithms have recently been developed and...
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Classification
This chapter returns to prediction. Unlike linear regression where we were predicting a numeric value, in this case we are predicting a class: winner...
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Accounting for class hierarchy in object classification using Siamese neural networks
Siamese neural networks are an effective architecture for automatic construction of vector representations of objects, by whose comparison it is...
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Robust multicategory support vector machines using difference convex algorithm
The support vector machine (SVM) is one of the most popular classification methods in the machine learning literature. Binary SVM methods have been...
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Aerodynamic shape optimization of wind turbine rotor blades using the continuous adjoint method
This paper presents the development of the continuous adjoint method for incompressible fluid flows, solved for the absolute velocity in the relative...
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Pairwise ranking with Gaussian kernel
Regularized pairwise ranking with Gaussian kernels is one of the cutting-edge learning algorithms. Despite a wide range of applications, a rigorous...
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Analysis of Audio-Based News Classification Using Machine Learning Techniques
Text classifiers can automatically analyse text using Natural Language Processing (NLP) techniques and then assign categories based on its content....
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Applications and Numerical Results
In this chapter we describe several real-life applications and provide results obtained by solving truss topology design (TTD), intensity-modulated...
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Features of Building a Microservice System for Modeling Nonstationary States of Nonlinear Circuits
AbstractThe main problems of configuring the dependences of software products built based on a microservice architecture are described. Examples of a...
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Jobs Runtime Forecast for JSCC RAS Supercomputers Using Machine Learning Methods
AbstractThe paper is devoted to machine learning methods and algorithms for the supercomputer jobs execution prediction. The supercomputers...
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Observability in the Univalent Universe
In this paper, we present a summary of Homotopy Type Theory with Voevodsky’s Univalent Axiom and discuss the existence of a universe of types...
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Difference of convex algorithms for bilevel programs with applications in hyperparameter selection
In this paper, we present difference of convex algorithms for solving bilevel programs in which the upper level objective functions are difference of...
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Improved Classification Rates for Localized Algorithms under Margin Conditions
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their...