Introduction to Sequence Analysis for Human Behavior Understanding

  • Hugues Salamin
  • Alessandro Vinciarelli


This chapter introduces the sequence analysis problem in machine learning. The problem is formulated in terms of two major issues: The first is the classification (assignment of a label to an entire sequence of observations), and the second is the labeling (assignment of a label to each observation in a sequence). The chapter applies the framework of probabilistic graphical models to introduce two of the most important sequence analysis models, namely Hidden Markov Models and Conditional Random Fields, with particular attention to their factorization and their underlying independence assumptions. The introduction is completed with some details about inference and training as well as some pointers to the literature.


Hide Markov Model Bayesian Network Independence Assumption Conditional Random Field Markov Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowScotland
  2. 2.Idiap Research InstituteMartignySwitzerland

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