Learnability of Timescale Graphical Event Models

Philipp Behrendt

Learnability of Timescale Graphical Event Models

Machine Learning 2020-05-26 v1 Machine Learning

Authors: Philipp Behrendt

Abstract

This technical report tries to fill a gap in current literature on Timescale Graphical Event Models. I propose and evaluate different heuristics to determine hyper-parameters during the structure learning algorithm and refine an existing distance measure. A comprehensive benchmark on synthetic data will be conducted allowing conclusions about the applicability of the different heuristics.

Keywords

temporal graph event extraction graph generation

Cite

@article{arxiv.2005.12186,
  title  = {Learnability of Timescale Graphical Event Models},
  author = {Philipp Behrendt},
  journal= {arXiv preprint arXiv:2005.12186},
  year   = {2020}
}

Comments

Technical Report

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