297 citations · 299 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…
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…