ML Anthology

Typology of Mixed-Membership Models: Towards a Design Method

Gregor Heinrich
ECML-PKDD 2011 pp. 32-47
doi:10.1007/978-3-642-23783-6_3 /ecmlpkdd/2011/heinrich2011ecmlpkdd-typology/

Abstract

Presents an analysis of the structure of mixed-membership models into elementary blocks and their numerical properties. By associating such model structures with structures known or assumed in the data, we propose how models can be constructed in a controlled way, using the numerical properties of data likelihood and Gibbs full conditionals as predictors of model behavior. To illustrate this “bottom-up” design method, example models are constructed that may be used for expertise finding from labeled data.

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Cite

Text

Heinrich. "Typology of Mixed-Membership Models: Towards a Design Method." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23783-6_3

Markdown

[Heinrich. "Typology of Mixed-Membership Models: Towards a Design Method." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/heinrich2011ecmlpkdd-typology/) doi:10.1007/978-3-642-23783-6_3

BibTeX

@inproceedings{heinrich2011ecmlpkdd-typology,
  title     = {{Typology of Mixed-Membership Models: Towards a Design Method}},
  author    = {Heinrich, Gregor},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2011},
  pages     = {32-47},
  doi       = {10.1007/978-3-642-23783-6_3},
  url       = {https://mlanthology.org/ecmlpkdd/2011/heinrich2011ecmlpkdd-typology/}
}