The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
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Citations
Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Self-citationNeural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 1×
“There is however a tension between these goals (Geman et al., 1992).”
BackgroundLearning Bayesian Networks: The Combination of Knowledge and Statistical Data
Heckerman, D., Geiger, D., & Chickering, D. M. (1994) · Uncertainty Proceedings 1994 · doi:10.1016/b978-1-55860-332-5.50042-0 · Cited by 261
Cited by
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Popular Ensemble Methods: An Empirical Study
Opitz, D., & Maclin, R. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.614 · Cited by 2,953
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
Wong, T.-T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.009 · Cited by 1,562
Activity Recognition in the Home Using Simple and Ubiquitous Sensors
Tapia, E. M., Intille, S. S., & Larson, K. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_10 · Cited by 1,323
eD octor: machine learning and the future of medicine
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956
Facial expression recognition from video sequences: temporal and static modeling
Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00081-x · Cited by 874
Classifier Technology and the Illusion of Progress
Hand, D. J. (2006) · Statistical Science · doi:10.1214/088342306000000060 · Cited by 723
Predictive Analytics in Information Systems Research
Shmueli, G., & Koppius, O. (2010) · doi:10.2139/ssrn.1606674 · Cited by 676
The Role of Occam's Razor in Knowledge Discovery
Domingos, P. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009868929893 · Cited by 459
UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
Han, X., Pasquier, T., Bates, A., Mickens, J., & Seltzer, M. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24046 · Cited by 349
From Machine Learning to Explainable AI
Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306
A critical review of algorithms in HRM: Definition, theory, and practice
Cheng, M. M., & Hackett, R. D. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2019.100698 · Cited by 283
Overfitting in linear feature extraction for classification of high-dimensional image data
Liu, R., & Gillies, D. F. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2015.11.015 · Cited by 123
Confidence-based classifier design
Li, M., & Sethi, I. K. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.01.010 · Cited by 61
A bias/variance decomposition for models using collective inference
Neville, J., & Jensen, D. (2008) · Machine Learning · doi:10.1007/s10994-008-5066-6 · Cited by 21
Examining Parent Versus Child Reviews of Parental Control Apps on Google Play
Alelyani, T., Ghosh, A. K., Moralez, L., Guha, S., & Wisniewski, P. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-21905-5_1 · Cited by 17
Metadata
- Authors
- Friedman, J. H.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 1997-03
- Volume
- 1
- Issue
- 1
- Pages
- 55-77
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W1840338487
- Cited by
- 1,040
- References
- 4
- FWCI
- 37.09
- 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.
