activity
20192025
most citedComprehensive Review On Twin Support Vector Machines

297 citations · 299 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

GBSVR: Granular Ball Support Vector Regression

Reshma Rastogi, Ankush Bisht, Sanjay Kumar +1

Support Vector Regression (SVR) and its variants are widely used to handle regression tasks, however, since their solution involves solving an expensive quadratic programming probl…

cs.LG2021

Efficient Learning of Pinball TWSVM using Privileged Information and its applications

Reshma Rastogi, Aman Pal

In any learning framework, an expert knowledge always plays a crucial role. But, in the field of machine learning, the knowledge offered by an expert is rarely used. Moreover, mach…

cs.LG2021

Improvement over Pinball Loss Support Vector Machine

Pritam Anand, Reshma Rastogi, Suresh Chandra

Recently, there have been several papers that discuss the extension of the Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by Huang et al.,[1][2]. Pin-SVM…

cs.LG2021★ 297 cited

Comprehensive Review On Twin Support Vector Machines

M. Tanveer, T. Rajani, R. Rastogi +2

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classific…

cs.LG2019

A - support vector quantile regression model with automatic accuracy control

Pritam Anand, Reshma Rastogi, Suresh Chandra

This paper proposes a novel '-support vector quantile regression' (-SVQR) model for the quantile estimation. It can facilitate the automatic control over accuracy by creating…

stat.ML2019

A new asymmetric -insensitive pinball loss function based support vector quantile regression model

Pritam Anand, Reshma Rastogi, Suresh Chandra

In this paper, we propose a novel asymmetric -insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate th…

cs.LG2019★ 2 cited

Support Vector Regression via a Combined Reward Cum Penalty Loss Function

Pritam Anand, Reshma Rastogi, Suresh Chandra

In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the…