Sparse solution of least-squares twin multi-class support vector machine using ℓ0 and ℓp-norm for classification and feature selection
In the realm of multi-class classification, the twin K-class support vector classification (Twin-
KSVC) generates ternary outputs {− 1, 0,+ 1} by evaluating all training data in a “1-versus-1 …
KSVC) generates ternary outputs {− 1, 0,+ 1} by evaluating all training data in a “1-versus-1 …
Profit-based churn prediction based on minimax probability machines
In this paper, we propose three novel profit-driven strategies for churn prediction. Our
proposals extend the ideas of the Minimax Probability Machine, a robust optimization …
proposals extend the ideas of the Minimax Probability Machine, a robust optimization …
Enhanced automatic twin support vector machine for imbalanced data classification
Most of the classification approaches assume that the sample distribution among classes is
balanced. Still, such an assumption leads to biased performance over the majority class …
balanced. Still, such an assumption leads to biased performance over the majority class …
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 …
Multi-task support vector machine with pinball loss
With the boom in machine learning, support vector machine (SVM) is widely employed in
pattern recognition. However, most of SVM models concentrate on single-task learning, multi …
pattern recognition. However, most of SVM models concentrate on single-task learning, multi …
[HTML][HTML] A novel robust optimization model for nonlinear Support Vector Machine
In this paper, we present new optimization models for Support Vector Machine (SVM), with
the aim of separating data points in two or more classes. The classification task is handled …
the aim of separating data points in two or more classes. The classification task is handled …
Epsilon-nonparallel support vector regression
In this work, a novel method called epsilon-nonparallel support vector regression (ε-NPSVR)
is proposed. The reasoning behind the nonparallel support vector machine (NPSVM) …
is proposed. The reasoning behind the nonparallel support vector machine (NPSVM) …
Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification
In this paper we present a Twin Parametric-Margin Support Vector Machine (TPMSVM)
model to tackle the problem of multiclass classification. In the spirit of one-versus-all …
model to tackle the problem of multiclass classification. In the spirit of one-versus-all …
A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification
Recent advances in healthcare technologies have led to the availability of large amounts of
biological samples across several techniques and applications. In particular, in the last few …
biological samples across several techniques and applications. In particular, in the last few …