Temporal maximum margin markov network

Xiaoqian Jiang, Bing Dong, Latanya Sweeney

Research output: Chapter in Book/Entry/PoemConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
Number of pages14
EditionPART 1
StatePublished - 2010
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, Spain
Duration: Sep 20 2010Sep 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6321 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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