14 citations · 16 across the 6 of their papers we have counts for
6 papers
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…
Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
Kartikeya Badola, Sameer Ambekar, Himanshu Pant +5
The gold standard for COVID-19 is RT-PCR, testing facilities for which are limited and not always optimally distributed. Test results are delayed, which impacts treatment. Expert r…
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…
Learning a hyperplane regressor by minimizing an exact bound on the VC dimension
Jayadeva, Suresh Chandra, Siddarth Sabharwal +1
The capacity of a learning machine is measured by its Vapnik-Chervonenkis dimension, and learning machines with a low VC dimension generalize better. It is well known that the VC d…