Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
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

Brun, André L., Britto, Alceu S., Oliveira, Luiz S., Enembreck, Fabricio, Sabourin, Robert (2018) A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recognition, 76. 175-190 doi:10.1016/j.patcog.2017.10.038

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleA framework for dynamic classifier selection oriented by the classification problem difficulty
JournalPattern Recognition
AuthorsBrun, André L.Author
Britto, Alceu S.Author
Oliveira, Luiz S.Author
Enembreck, FabricioAuthor
Sabourin, RobertAuthor
Year2018 (April)Volume76
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2017.10.038Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367256Long-form Identifiermindat:1:5:9367256:6
GUID0
Full ReferenceBrun, André L., Britto, Alceu S., Oliveira, Luiz S., Enembreck, Fabricio, Sabourin, Robert (2018) A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recognition, 76. 175-190 doi:10.1016/j.patcog.2017.10.038
Plain TextBrun, André L., Britto, Alceu S., Oliveira, Luiz S., Enembreck, Fabricio, Sabourin, Robert (2018) A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recognition, 76. 175-190 doi:10.1016/j.patcog.2017.10.038
In(2018) Pattern Recognition Vol. 76. Elsevier BV

See Also

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

Britto, Alceu S., Sabourin, Robert, Oliveira, Luiz E.S. (2014) Dynamic selection of classifiers—A comprehensive review. Pattern Recognition, 47 (11). 3665-3680 doi:10.1016/j.patcog.2014.05.003
Ko, Albert H.R., Sabourin, Robert, Britto, Jr., Alceu Souza (2008) From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition, 41 (5). 1718-1731 doi:10.1016/j.patcog.2007.10.015
Assumpção Silva, Ronan, Britto, Alceu S., Enembreck, Fabricio, Sabourin, Robert, Oliveira, Luiz S. (2020) Selecting and Combining Classifiers Based on Centrality Measures. International Journal on Artificial Intelligence Tools, 29 (3) 2060004pp. doi:10.1142/s0218213020600040
Ko, Albert H.R., Sabourin, Robert, Britto, Alceu de Souza, Oliveira, Luiz (2007) Pairwise fusion matrix for combining classifiers. Pattern Recognition, 40 (8). 2198-2210 doi:10.1016/j.patcog.2007.01.031
Silva, Ronan Assumpção, Britto Jr, Alceu de Souza, Enembreck, Fabricio, Sabourin, Robert, Oliveira, Luiz E. S. (2020) CSBF: A static ensemble fusion method based on the centrality score of complex networks. Computational Intelligence, 36 (2) 522-556 doi:10.1111/coin.12249
Cavalin, Paulo R., Sabourin, Robert, Suen, Ching Y., Britto Jr., Alceu S. (2009) Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters. Pattern Recognition, 42 (12). 3241-3253 doi:10.1016/j.patcog.2008.10.012
Souza, Mariana A., Cavalcanti, George D.C., Cruz, Rafael M.O., Sabourin, Robert (2019) Online local pool generation for dynamic classifier selection. Pattern Recognition, 85. 132-148 doi:10.1016/j.patcog.2018.08.004
Dos Santos, Eulanda M., Sabourin, Robert, Maupin, Patrick (2008) A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recognition, 41 (10). 2993-3009 doi:10.1016/j.patcog.2008.03.027
Hafemann, Luiz G., Sabourin, Robert, Oliveira, Luiz S. (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognition, 70. 163-176 doi:10.1016/j.patcog.2017.05.012
Oliveira, Dayvid V.R., Cavalcanti, George D.C., Sabourin, Robert (2017) Online pruning of base classifiers for Dynamic Ensemble Selection. Pattern Recognition, 72. 44-58 doi:10.1016/j.patcog.2017.06.030
KO, ALBERT HUNG-REN, SABOURIN, ROBERT, DE SOUZA BRITTO, ALCEU (2009) COMPOUND DIVERSITY FUNCTIONS FOR ENSEMBLE SELECTION. International Journal of Pattern Recognition and Artificial Intelligence, 23. 659-686 doi:10.1142/s021800140900734x
 
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 © 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: August 24, 2025 02:16:49
Go to top of page