# Marginal Structured SVM with Hidden Variables

arXiv: https://arxiv.org/abs/1409.1320 · HTML: https://mlsanity.com/paper/1409.1320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [Constrained Structure Learning for Scene Graph Generation](https://mlsanity.com/paper/2201.11697/) (2022)

## Abstract (arXiv)

In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and can significantly outperform the previously proposed latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art methods, especially when that uncertainty is large. Our method also results in a smoother objective function, making gradient-based optimization of MSSVMs converge significantly faster than for LSSVMs. We also show that our method consistently outperforms hidden conditional random fields (HCRFs; Quattoni et al. (2007)) on both simulated and real-world datasets. Furthermore, we propose a unified framework that includes both our and several other existing methods as special cases, and provides insights into the comparison of different models in practice.