Conference paper

KRÁL Jiří and HRADIŠ Michal. Restricted Boltzman Machines for Image Tag Suggestion. In: Proceedings of the 19th Conference STUDENT EEICT 2012. Brno: Brno University of Technology, 2012, p. 5.
Publication language:english
Original title:Restricted Boltzman Machines for Image Tag Suggestion
Pages:5
Proceedings:Proceedings of the 19th Conference STUDENT EEICT 2012
Conference:Student EEICT 2012
Place:Brno, CZ
Year:2012
Publisher:Brno University of Technology
URL:http://www.feec.vutbr.cz/EEICT/2012/sbornik/03doktorskeprojekty/09grafikaamultimedia/03-ikral.pdf [PDF]
Annotation
In this paper, we propose to model dependencies among binary variables in semantic tagging and similar tasks by Restricted Boltzmann Machines (RBM). In the proposed approach, Gibbs sampling allows learning RBMs even on data with large portion of missing values. Similarly, Gibbs sampling is used to estimate marginal probabilities of tags. The results show that the tag predictions become more certain with higher portion of known tags, and that the approach could be used for tag suggestion or semi-supervised learning.
BibTeX:
@INPROCEEDINGS{
   author = {Ji{\v{r}}{\'{i}} Kr{\'{a}}l and Michal Hradi{\v{s}}},
   title = {Restricted Boltzman Machines for Image Tag Suggestion},
   pages = {5},
   booktitle = {Proceedings of the 19th Conference STUDENT EEICT 2012},
   year = {2012},
   location = {Brno, CZ},
   publisher = {Brno University of Technology},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9976}
}

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