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Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations

  • Maximilian PanznerEmail author
  • Philipp Cimiano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

In this paper we are concerned with learning models of actions and compare a purely generative model based on Hidden Markov Models to a discriminatively trained recurrent LSTM network in terms of their properties and their suitability to learn and represent models of actions. Specifically we compare the performance of the two models regarding the overall classification accuracy, the amount of training sequences required and how early in the progression of a sequence they are able to correctly classify the corresponding sequence. We show that, despite the current trend towards (deep) neural networks, traditional graphical model approaches are still beneficial under conditions where only few data points or limited computing power is available.

Keywords

HMM LSTM Incremental learning Recurrent network Action classification 

Notes

Acknowledgement

This research/work was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Semantic Computing Group, CITECBielefeld UniversityBielefeldGermany

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