5 papers
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…
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…
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…
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…