1 code implementation • International Conference on Computer Vision and Machine Intelligence (CVMI-2022) 2023 • Sanjay Kumar, Reshma Rastogi
Label correlation has been exploited for multi-label learning in different ways.
1 code implementation • Applied Intelligence Journal 2023 • Sanjay Kumar, Nadira Ahmadi, Reshma Rastogi
Multi-label learning associates a given data instance with one or several class labels.
1 code implementation • Neural Processing Letters 2022 • Reshma Rastogi, Sanjay Kumar
Class labels in multi-label datasets are only associated with a very small fraction of the data instances leading to a class imbalance problem.
1 code implementation • Information Sciences 2022 • Sanjay Kumar, Reshma Rastogi
The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML).
no code implementations • 14 Jul 2021 • Reshma Rastogi, Aman Pal
Thus, in order to get the advantage from an expert knowledge and to reduce the sensitivity towards the noise, in this paper, we propose privileged information based Twin Pinball Support Vector Machine classifier (Pin-TWSVMPI) where expert's knowledge is in the form of privileged information.
1 code implementation • 2 Jun 2021 • Pritam Anand, Reshma Rastogi, Suresh Chandra
The existing Pin-SVM model requires to solve the same optimization problem for all values of $\tau$ in $[ -1, 1]$.
no code implementations • 21 Oct 2019 • Pritam Anand, Reshma Rastogi, Suresh Chandra
The proposed $\nu$-SVQR model uses the $\nu$ fraction of training data points for the estimation of the quantiles.
no code implementations • 19 Aug 2019 • Pritam Anand, Reshma Rastogi, Suresh Chandra
In this paper, we propose a novel asymmetric $\epsilon$-insensitive pinball loss function for quantile estimation.
no code implementations • 28 Apr 2019 • Pritam Anand, Reshma Rastogi, Suresh Chandra
In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem.