On a possibility of gradual model-learning

Radim Jiroušek
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:245-256, 2020.

Abstract

In this paper, the term of gradual learning describes the process, in which an $n$-dimensional model is constructed in $n$ steps; each step increases the dimensionality of the constructed model by one. The approach is explained using the apparatus of compositional models since its algebraic properties seem to serve the purpose best. The paper shows also the equivalence of compositional models and Bayesian networks, and thus the paper gives a hint that the approach applies to the graphical model as well.

Cite this Paper

BibTeX
@InProceedings{pmlr-v138-jirousek20a, title = {On a possibility of gradual model-learning}, author = {Jirou\v{s}ek, Radim}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {245--256}, year = {2020}, editor = {Jaeger, Manfred and Nielsen, Thomas Dyhre}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/jirousek20a/jirousek20a.pdf}, url = {https://proceedings.mlr.press/v138/jirousek20a.html}, abstract = {In this paper, the term of gradual learning describes the process, in which an $n$-dimensional model is constructed in $n$ steps; each step increases the dimensionality of the constructed model by one. The approach is explained using the apparatus of compositional models since its algebraic properties seem to serve the purpose best. The paper shows also the equivalence of compositional models and Bayesian networks, and thus the paper gives a hint that the approach applies to the graphical model as well. } }
Endnote
%0 Conference Paper %T On a possibility of gradual model-learning %A Radim Jiroušek %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-jirousek20a %I PMLR %P 245--256 %U https://proceedings.mlr.press/v138/jirousek20a.html %V 138 %X In this paper, the term of gradual learning describes the process, in which an $n$-dimensional model is constructed in $n$ steps; each step increases the dimensionality of the constructed model by one. The approach is explained using the apparatus of compositional models since its algebraic properties seem to serve the purpose best. The paper shows also the equivalence of compositional models and Bayesian networks, and thus the paper gives a hint that the approach applies to the graphical model as well.
APA
Jiroušek, R.. (2020). On a possibility of gradual model-learning. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:245-256 Available from https://proceedings.mlr.press/v138/jirousek20a.html.

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