# Learning Using Privileged Information: SVM+ and Weighted SVM

arXiv: https://arxiv.org/abs/1306.3161 · HTML: https://mlsanity.com/paper/1306.3161/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2013.

## Built on by (most cited first, 13 of 13)

- [Toward Understanding Privileged Features Distillation in Learning-to-Rank](https://mlsanity.com/paper/2209.08754/) (2022)
- [Transfer and Marginalize: Explaining Away Label Noise with Privileged Information](https://mlsanity.com/paper/2202.09244/) (2022)
- [Testing using Privileged Information by Adapting Features with Statistical Dependence](https://mlsanity.com/paper/2111.02865/) (2021)
- [PEPR: Privileged Event-based Predictive Regularization for Domain Generalization](https://mlsanity.com/paper/2602.04583/) (2026)
- [Efficient Learning of Pinball TWSVM using Privileged Information and its applications](https://mlsanity.com/paper/2107.06744/) (2021)
- [A New Learning Paradigm for Stochastic Configuration Network: SCN+](https://mlsanity.com/paper/2203.07068/) (2022)
- [Classification by estimating the cumulative distribution function for small data](https://mlsanity.com/paper/2210.05953/) (2022)
- [On the Capacity Limits of Privileged ERM](https://mlsanity.com/paper/2303.02658/) (2023)
- [A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training](https://mlsanity.com/paper/2309.03209/) (2023)
- [On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning](https://mlsanity.com/paper/2404.02254/) (2024)
- [Local Duality for Sparse Support Vector Machines](https://mlsanity.com/paper/2601.20170/) (2026)
- [Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information](https://mlsanity.com/paper/2606.22210/) (2026)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)

## Abstract (arXiv)

Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time -- a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.