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Statistical Pattern Recognition
Abstract
Statistical pattern recognition is an application in computational statistics that uses many of the concepts we have covered so far, such as probability density estimation and cross-validation. Examples where statistical pattern recognition techniques can be used are numerous and arise in disciplines such as medicine, computer vision, robotics, military systems, manufacturing, finance and many others. Some of these include the following: • A doctor diagnoses a patient’s illness based on the symptoms and test results.
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Cited by
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
A review of feature selection methods based on mutual information
Vergara, J. R., & Estévez, P. A. (2014) · Neural Computing and Applications · doi:10.1007/s00521-013-1368-0 · Cited by 1,255
Robust real-time face detection
Viola, P., & Jones, M. (2005) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937709 · Cited by 898
Metadata
- Venue
- Computational Statistics Handbook with MATLAB
- Publisher
- Chapman and Hall/CRC
- Published
- 2001-09-26
- Pages
- 335-402
- OpenAlex
- W3146003712
- Cited by
- 1,740
- References
- 0
- FWCI
- 93.73
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Statistical Pattern Recognition. (2001). Computational Statistics Handbook with MATLAB. https://doi.org/10.1201/9781420035636-10
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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.
