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.

本文提出了边际结构化支持向量机（MSSVM）来进行具有隐藏变量的结构化预测，与其他最先进的方法相比，特别是当不确定性很大时，MSSVM可以更好地解释隐藏变量的不确定性，并且导致更平滑的目标函数，使梯度下降方法更快地收敛。我们还展示了这种方法如何在模拟和实际数据集上优于其他方法，包括潜在结构化SVM（LSSVM）和隐藏条件随机场（HCRFs）。此外，我们提出了一个统一框架，将我们的方法与其他现有方法作为特例，以提供对不同模型在实践中的比较的见解。

带隐变量的边际结构支持向量机