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Found 9 papers, 5 papers with code
Auxiliary Label Embedding for Multi-label Learning with Missing Labels
Label correlation has been exploited for multi-label learning in different ways.
Multi-label learning with missing labels using sparse global structure for label-specific features
Multi-label learning associates a given data instance with one or several class labels.
Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels
Class labels in multi-label datasets are only associated with a very small fraction of the data instances leading to a class imbalance problem.
Low rank label subspace transformation for multi-label learning with missing labels
The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML).
Efficient Learning of Pinball TWSVM using Privileged Information and its applications
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.
Improvement over Pinball Loss Support Vector Machine
The existing Pin-SVM model requires to solve the same optimization problem for all values of $\tau$ in $[ -1, 1]$.
A $ν$- support vector quantile regression model with automatic accuracy control
The proposed $\nu$-SVQR model uses the $\nu$ fraction of training data points for the estimation of the quantiles.
A new asymmetric $ε$-insensitive pinball loss function based support vector quantile regression model
In this paper, we propose a novel asymmetric $\epsilon$-insensitive pinball loss function for quantile estimation.
Support Vector Regression via a Combined Reward Cum Penalty Loss Function
In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem.
