Learnability of Timescale Graphical Event Models
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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2020
- DOI:
- 10.48550/arXiv.2005.12186
- arXiv:
- arXiv:2005.12186
- Bibcode:
- 2020arXiv200512186B
- Keywords:
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- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- Technical Report