One-class support vector classifiers: A survey
Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …
diversified applicability in data mining and pattern recognition problems. Concerning to …
DC programming and DCA: thirty years of developments
The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
Fast SVM classifier for large-scale classification problems
Support vector machines (SVM), as one of effective and popular classification tools, have
been widely applied in various fields. However, they may incur prohibitive computational …
been widely applied in various fields. However, they may incur prohibitive computational …
Robust statistics-based support vector machine and its variants: a survey
M Singla, KK Shukla - Neural Computing and Applications, 2020 - Springer
Support vector machines (SVMs) are versatile learning models which are used for both
classification and regression. Several authors have reported successful applications of SVM …
classification and regression. Several authors have reported successful applications of SVM …
[HTML][HTML] A mathematical programming approach to SVM-based classification with label noise
In this paper we propose novel methodologies to optimally construct Support Vector
Machine-based classifiers that take into account that label noise occur in the training …
Machine-based classifiers that take into account that label noise occur in the training …
Support Vector Machine Classifier via Soft-Margin Loss
Support vector machines (SVM) have drawn wide attention for the last two decades due to
its extensive applications, so a vast body of work has developed optimization algorithms to …
its extensive applications, so a vast body of work has developed optimization algorithms to …
Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem
Network intrusion detection problem is an ongoing challenging research area because of a
huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks …
huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks …
Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems
Anomaly detection defines as a problem of finding those data samples, which do not follow
the patterns of the majority of data points. Among the variety of methods and algorithms …
the patterns of the majority of data points. Among the variety of methods and algorithms …
Distributionally favorable optimization: A framework for data-driven decision-making with endogenous outliers
A typical data-driven stochastic program seeks the best decision that minimizes the sum of a
deterministic cost function and an expected recourse function under a given distribution …
deterministic cost function and an expected recourse function under a given distribution …
Fast truncated Huber loss SVM for large scale classification
H Wang, Y Shao - Knowledge-Based Systems, 2023 - Elsevier
Support vector machine (SVM), as a useful tool of classification, has been widely applied in
many fields. However, it may incur computationally infeasibility on very large sample …
many fields. However, it may incur computationally infeasibility on very large sample …