Vote for your favorite mineral in #MinCup25! - Silver vs. Baryte
Are you ready for beautiful utility as sparkling silver competes against hefty baryte?
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Liu, Li, Wang, Shu, Hu, Bin, Qiong, Qingyu, Wen, Junhao, Rosenblum, David S. (2018) Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition. Pattern Recognition, 81. 545-561 doi:10.1016/j.patcog.2018.04.022

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleLearning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition
JournalPattern Recognition
AuthorsLiu, LiAuthor
Wang, ShuAuthor
Hu, BinAuthor
Qiong, QingyuAuthor
Wen, JunhaoAuthor
Rosenblum, David S.Author
Year2018 (September)Volume81
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2018.04.022Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367463Long-form Identifiermindat:1:5:9367463:4
GUID0
Full ReferenceLiu, Li, Wang, Shu, Hu, Bin, Qiong, Qingyu, Wen, Junhao, Rosenblum, David S. (2018) Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition. Pattern Recognition, 81. 545-561 doi:10.1016/j.patcog.2018.04.022
Plain TextLiu, Li, Wang, Shu, Hu, Bin, Qiong, Qingyu, Wen, Junhao, Rosenblum, David S. (2018) Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition. Pattern Recognition, 81. 545-561 doi:10.1016/j.patcog.2018.04.022
In(2018) Pattern Recognition Vol. 81. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Liu, Li, Wang, Shu, Su, Guoxin, Huang, Zi-Gang, Liu, Ming (2017) Towards complex activity recognition using a Bayesian network-based probabilistic generative framework. Pattern Recognition, 68. 295-309 doi:10.1016/j.patcog.2017.02.028
Liu, Li, Wang, Shu, Su, Guoxin, Hu, Bin, Peng, Yuxin, Xiong, Qingyu, Wen, Junhao (2017) A framework of mining semantic-based probabilistic event relations for complex activity recognition. Information Sciences, 418. 13-33 doi:10.1016/j.ins.2017.07.022
Hu, Chunyu, Chen, Yiqiang, Hu, Lisha, Peng, Xiaohui (2018) A novel random forests based class incremental learning method for activity recognition. Pattern Recognition, 78. 277-290 doi:10.1016/j.patcog.2018.01.025
Liu, Li, Wang, Shu, Peng, Yuxin, Huang, Zigang, Liu, Ming, Hu, Bin (2016) Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty. Pattern Recognition, 60. 1015-1028 doi:10.1016/j.patcog.2016.07.024
Wang, Hongsong, Wang, Liang (2018) Learning content and style: Joint action recognition and person identification from human skeletons. Pattern Recognition, 81. 23-35 doi:10.1016/j.patcog.2018.03.030
Bouguila, Nizar (2011) Bayesian hybrid generative discriminative learning based on finite Liouville mixture models. Pattern Recognition, 44 (6). 1183-1200 doi:10.1016/j.patcog.2010.12.010
Wang, Siyang, Wang, Lin, Liu, Wenyuan (2024) Feature decoupling and regeneration towards wifi-based human activity recognition. Pattern Recognition, 153. 110480 doi:10.1016/j.patcog.2024.110480
Hu, Zhengping, Li, Zijun, Wang, Xueyu, Zheng, Saiyue (2022) Unsupervised descriptor selection based meta-learning networks for few-shot classification. Pattern Recognition, 122. 108304pp. doi:10.1016/j.patcog.2021.108304
Ding, Xin, Fang, Jing, Wang, Zhongyuan, Liu, Qiong, Yang, You, Shu, Zhenyu (2025) Unsupervised learning non-uniform face enhancement under physics-guided model of illumination decoupling. Pattern Recognition, 162. doi:10.1016/j.patcog.2025.111354
Wang, Qiong, Zhang, Lu, Li, Yan, Kpalma, Kidiyo (2020) Overview of deep-learning based methods for salient object detection in videos. Pattern Recognition, 104. 107340pp. doi:10.1016/j.patcog.2020.107340
Xie, Yu, Gong, Maoguo, Wang, Shanfeng, Yu, Bin (2018) Community discovery in networks with deep sparse filtering. Pattern Recognition, 81. 50-59 doi:10.1016/j.patcog.2018.03.026
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 5, 2025 11:07:47
Go to top of page