Vote for your favorite mineral in #MinCup25! - Chrysotile vs. Pectolite
Two new minerals to launch this year's cup! Hazardous (but useful!) chrysotile faces off against stunning gem pectolite.
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

Li, Yanchao, Wang, Yongli, Liu, Qi, Bi, Cheng, Jiang, Xiaohui, Sun, Shurong (2019) Incremental semi-supervised learning on streaming data. Pattern Recognition, 88. 383-396 doi:10.1016/j.patcog.2018.11.006

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleIncremental semi-supervised learning on streaming data
JournalPattern Recognition
AuthorsLi, YanchaoAuthor
Wang, YongliAuthor
Liu, QiAuthor
Bi, ChengAuthor
Jiang, XiaohuiAuthor
Sun, ShurongAuthor
Year2019 (April)Volume88
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2018.11.006Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367645Long-form Identifiermindat:1:5:9367645:6
GUID0
Full ReferenceLi, Yanchao, Wang, Yongli, Liu, Qi, Bi, Cheng, Jiang, Xiaohui, Sun, Shurong (2019) Incremental semi-supervised learning on streaming data. Pattern Recognition, 88. 383-396 doi:10.1016/j.patcog.2018.11.006
Plain TextLi, Yanchao, Wang, Yongli, Liu, Qi, Bi, Cheng, Jiang, Xiaohui, Sun, Shurong (2019) Incremental semi-supervised learning on streaming data. Pattern Recognition, 88. 383-396 doi:10.1016/j.patcog.2018.11.006
In(2019) Pattern Recognition Vol. 88. Elsevier BV

See Also

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

Liu, Wenzhuo, Wu, Xin-Jian, Zhu, Fei, Yu, Ming-Ming, Wang, Chuang, Liu, Cheng-Lin (2025) Class incremental learning with self-supervised pre-training and prototype learning. Pattern Recognition, 157. doi:10.1016/j.patcog.2024.110943
Li, Jian, Liu, Yong, Wang, Weiping (2023) Semi-supervised vector-valued learning: Improved bounds and algorithms. Pattern Recognition, 138. 109356 doi:10.1016/j.patcog.2023.109356
Lai, Danyu, Tian, Wei, Chen, Long (2019) Improving classification with semi-supervised and fine-grained learning. Pattern Recognition, 88. 547-556 doi:10.1016/j.patcog.2018.12.002
Yan, Wenzhu, Sun, Quansen, Sun, Huaijiang, Li, Yanmeng (2020) Semi-supervised learning framework based on statistical analysis for image set classification. Pattern Recognition, 107. 107500pp. doi:10.1016/j.patcog.2020.107500
Cheng, Jian, Wang, Jinjun, Jiang, Shuqiang, Zhou, Zhi-Hua, Hancock, Edwin (2011) Special edition on semi-supervised learning for visual content analysis and understanding. Pattern Recognition, 44 (10). 2242-2243 doi:10.1016/j.patcog.2011.05.001
Dhamecha, Tejas Indulal, Singh, Richa, Vatsa, Mayank (2016) On incremental semi-supervised discriminant analysis. Pattern Recognition, 52. 135-147 doi:10.1016/j.patcog.2015.09.030
Hu, Kai, Gong, Sheng, Li, Lingxiao, Luo, Yuantu, Li, YaoGen, Jiang, Shanshan (2025) Federated Incremental Learning algorithm based on Topological Data Analysis. Pattern Recognition, 158. doi:10.1016/j.patcog.2024.111048
Wang, Yukun, Gu, Shuo, Liu, Yinbo, Kong, Hui (2024) Semi-supervised vanishing point detection with contrastive learning. Pattern Recognition, 153. 110518 doi:10.1016/j.patcog.2024.110518
Liu, Ruyu; Xiao, Feng; Zhang, Jianhua; Liu, Xiufeng; Cheng, Xu; Chen, Shengyong; Sun, Bo; Zhang, Houxiang (2026) Multi-branch perturbation learning with constraint simulation for semi-supervised semantic segmentation. Pattern Recognition, 171. doi:10.1016/j.patcog.2025.112200
Wang, Qianying, Yuen, Pong C, Feng, Guocan (2013) Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions. Pattern Recognition, 46 (9). 2576-2587 doi:10.1016/j.patcog.2013.02.015
Xu, Fudong, Wang, Lin, Li, Huibin (2024) A unified and efficient semi-supervised learning framework for stereo matching. Pattern Recognition, 147. 110128 doi:10.1016/j.patcog.2023.110128
 
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 1, 2025 14:27:16
Go to top of page