The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Learning drifting concepts: Example selection vs. example weighting
Abstract
For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change. A typical example is information filtering, i.e. the adaptive classification of documents with respect to a particular u
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Cites
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Term-weighting approaches in automatic text retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 9,740
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
NewsWeeder: Learning to Filter Netnews
Lang, K. (1995) · Machine Learning Proceedings 1995 · doi:10.1016/b978-1-55860-377-6.50048-7 · Cited by 2,051
Learning in the Presence of Concept Drift and Hidden Contexts
Widmer, G., & Kubat, M. (1996) · Machine Learning · doi:10.1023/a:1018046501280 · Cited by 1,857
Incremental and Decremental Support Vector Machine Learning
Cauwenberghs, G., & Poggio, T. (2000) · Cited by 1,147
Web Watcher: A Tour Guide for the World Wide Web.
Joachims, T., Freitag, D., & Mitchell, T. M. (1997) · Cited by 685
Incremental learning with support vector machines
Ruping, S. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989589 · Cited by 524
Detecting Concept Drift with Support Vector Machines
Klinkenberg, R., & Joachims, T. (2000) · Cited by 422
Self-citationExperience with a learning personal assistant
Mitchell, T. M., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994) · Communications of the ACM · doi:10.1145/176789.176798 · Cited by 414
Cited by
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Gama, J., Medas, P., Castillo, G., & Rodrigues, P. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-28645-5_29 · Cited by 1,536
Ensemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050
A unifying view on dataset shift in classification
Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., & Herrera, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.06.019 · Cited by 1,029
On evaluating stream learning algorithms
Gama, J., Sebastião, R., & Rodrigues, P. P. (2013) · Machine Learning · doi:10.1007/s10994-012-5320-9 · Cited by 532
Metadata
- Authors
- Klinkenberg, R.
- Venue
- Intelligent Data Analysis
- Publisher
- SAGE Publications
- Published
- 2004-08-13
- Volume
- 8
- Issue
- 3
- Pages
- 281-300
- ISSN
- 1088-467X (ISSN-L)
- 1571-4128
- OpenAlex
- W2133088989
- Cited by
- 438
- References
- 21
- FWCI
- 17.28
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
