Temporal Maximum Margin Markov Network

  • Xiaoqian Jiang
  • Bing Dong
  • Latanya Sweeney
Conference paper

DOI: 10.1007/978-3-642-15880-3_43

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6321)
Cite this paper as:
Jiang X., Dong B., Sweeney L. (2010) Temporal Maximum Margin Markov Network. In: Balcázar J.L., Bonchi F., Gionis A., Sebag M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science, vol 6321. Springer, Berlin, Heidelberg

Abstract

Typical structured learning models consist of a regression component of the explanatory variables (observations) and another regression component that accounts for the neighboring states. Such models, including Conditional Random Fields (CRFs) and Maximum Margin Markov Network (M3N), are essentially Markov random fields with the pairwise spatial dependence. They are effective tools for modeling spatial correlated responses; however, ignoring the temporal correlation often limits their performance to model the more complex scenarios. In this paper, we introduce a novel Temporal Maximum Margin Markov Network (TM3N) model to learn the spatial-temporal correlated hidden states, simultaneously. For learning, we estimate the model’s parameters by leveraging on loopy belief propagation (LBP); for predicting, we forecast hidden states use linear integer programming (LIP); for evaluation, we apply TM3N to the simulated datasets and the real world challenge for occupancy estimation. The results are compared with other state-of-the-art models and demonstrate superior performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiaoqian Jiang
    • 1
  • Bing Dong
    • 2
  • Latanya Sweeney
    • 1
  1. 1.Data Privacy Lab, School of Computer Science 
  2. 2.Center for Building Performance and Diagnostics, School of ArchitectureCarnegie Mellon University 

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