Search Machine Learning Repository: @inproceedings{icml2014c2_ping14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Marginal Structured SVM with Hidden Variables},
    Url = {http://jmlr.org/proceedings/papers/v32/ping14.pdf},
    Abstract = {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.},
    Author = {Wei Ping and Qiang Liu and Alex Ihler},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {190-198}
   }