%Aigaion2 BibTeX export from Idiap Publications %Friday 27 December 2024 04:04:41 AM @INPROCEEDINGS{zhang-rr-05-48b, author = {Zhang, Dong and Gatica-Perez, Daniel and Bengio, Samy and Roy, Deb}, projects = {Idiap}, title = {{Learning influence among interacting Markov chains}}, booktitle = {NIPS}, year = {2005}, note = {IDIAP-RR 05-48}, crossref = {zhang-rr-05-48}, abstract = {We present a model that learns the influence of interacting Markov chains within a team. The proposed model is a dynamic Bayesian network (DBN) with a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. Experiments on synthetic multi-player games and a multi-party meeting corpus show the effectiveness of the proposed model.}, pdf = {https://publications.idiap.ch/attachments/reports/2005/zhang-nips-05.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2005/rr-05-48.ps.gz}, ipdmembership={vision}, } crossreferenced publications: @TECHREPORT{zhang-rr-05-48, author = {Zhang, Dong and Gatica-Perez, Daniel and Bengio, Samy and Roy, Deb}, projects = {Idiap}, title = {{Learning influence among interacting Markov chains}}, type = {Idiap-RR}, number = {Idiap-RR-48-2005}, year = {2005}, institution = {IDIAP}, address = {Martigny, Switzerland}, note = {Published in NIPS, Dec, 2005}, abstract = {We present a model that learns the influence of interacting Markov chains within a team. The proposed model is a dynamic Bayesian network (DBN) with a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. Experiments on synthetic multi-player games and a multi-party meeting corpus show the effectiveness of the proposed model.}, pdf = {https://publications.idiap.ch/attachments/reports/2005/rr-05-48.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2005/rr-05-48.ps.gz}, ipdinar={2005}, ipdmembership={vision}, language={English}, }