The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Classification and Regression Trees, Bagging, and Boosting
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674
Cited by
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887
Machine Learning Methods Without Tears: A Primer for Ecologists
Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823
Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.064 · Cited by 787
Modeling Species Distribution and Change Using Random Forest
Evans, J. S., Murphy, M. A., Holden, Z. A., & Cushman, S. A. (2011) · Predictive Species and Habitat Modeling in Landscape Ecology · doi:10.1007/978-1-4419-7390-0_8 · Cited by 364
Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F., & Kløve, B. (2018) · Science of The Total Environment · doi:10.1016/j.scitotenv.2017.09.293 · Cited by 292
Metadata
- Authors
- Sutton, C. D.
- Venue
- Handbook of Statistics
- Publisher
- Elsevier
- Published
- 2005
- Pages
- 303-329
- ISSN
- 0169-7161 (ISSN-L)
- OpenAlex
- W1584236903
- Cited by
- 587
- References
- 10
- FWCI
- 4.79
- Type
- book chapter
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
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- CoChat. (2026). Citation check: Classification and Regression Trees, Bagging, and Boosting. https://app.cochat.ai/papers/10.1016/s0169-7161(04)24011-1
- The paper
- Sutton, C. D. (2005). Classification and Regression Trees, Bagging, and Boosting. Handbook of Statistics. https://doi.org/10.1016/s0169-7161(04)24011-1
Data sources
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.
