Recent advances in robust optimization: An overview
This paper provides an overview of developments in robust optimization since 2007. It seeks
to give a representative picture of the research topics most explored in recent years …
to give a representative picture of the research topics most explored in recent years …
Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization
Uncertainties from deepening penetration of renewable energy resources have posed
critical challenges to the secure and reliable operations of future electric grids. Among …
critical challenges to the secure and reliable operations of future electric grids. Among …
A survey of nonlinear robust optimization
Robust optimization (RO) has attracted much attention from the optimization community over
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …
An imprecise extension of SVM-based machine learning models
LV Utkin - Neurocomputing, 2019 - Elsevier
A general approach for incorporating imprecise prior knowledge and for robustifying the
machine learning SVM-based models is proposed in the paper. The main idea underlying …
machine learning SVM-based models is proposed in the paper. The main idea underlying …
Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis
This paper addresses the problem of classification when target data are subject to feature
uncertainties. A robust approach based on Sobol sensitivity analysis is proposed to improve …
uncertainties. A robust approach based on Sobol sensitivity analysis is proposed to improve …
Robust optimization: concepts and applications
J García, A Peña - Nature-inspired methods for stochastic, robust …, 2018 - books.google.com
Robust optimization is an emerging area in research that allows addressing different
optimization problems and specifically industrial optimization problems where there is a …
optimization problems and specifically industrial optimization problems where there is a …
Cost-sensitive feature selection for support vector machines
Feature Selection is a crucial procedure in Data Science tasks such as Classification, since
it identifies the relevant variables, making thus the classification procedures more …
it identifies the relevant variables, making thus the classification procedures more …
A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence
This research proposes a new hybrid approach for feature selection and Support Vector
Machine (SVM) model selection based on a new variation of Cohort Intelligence (CI) …
Machine (SVM) model selection based on a new variation of Cohort Intelligence (CI) …
A survey of robust optimization based machine learning with special reference to support vector machines
M Singla, D Ghosh, KK Shukla - International Journal of Machine Learning …, 2020 - Springer
This paper gives an overview of developments in the field of robust optimization in machine
learning (ML) in general and Support Vector Machine (SVM)/Support Vector Regression …
learning (ML) in general and Support Vector Machine (SVM)/Support Vector Regression …
[HTML][HTML] Robust and distributionally robust optimization models for linear support vector machine
In this paper we present novel data-driven optimization models for Support Vector Machines
(SVM), with the aim of linearly separating two sets of points that have non-disjoint convex …
(SVM), with the aim of linearly separating two sets of points that have non-disjoint convex …