The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Effects of sample size in classifier design
Abstract
The effect of finite sample-size on parameter estimates and their subsequent use in a family of functions are discussed. General and parameter-specific expressions for the expected bias and variance of the functions are derived. These expressions are then applied to the Bhattacharyya distance and the analysis of the linear and quadratic classifiers, providing insight into the relationship between the number of features and the number of training samples. Because of the functional form of the expressions, an empirical approach is presented to enable asymptotic performance to be accurately estimated using a very small number of samples. Results were experimentally verified using artificial data in controlled cases and using real, high-dimensional data.>
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Cites
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
Introduction to statistical pattern recognition (2nd ed.)
Fukunaga, K. (1990) · Academic Press Professional, Inc. eBooks · Cited by 3,797
Self-citationIntroduction to Statistical Pattern Recognition
(2011) · Statistical Pattern Recognition · doi:10.1002/9781119952954.ch1 · Cited by 1,597
C.R.C. Standard Mathematical Tables
Crank, J. (1962) · Physics Bulletin · doi:10.1088/0031-9112/13/4/008 · Cited by 608
39 Dimensionality and sample size considerations in pattern recognition practice
Jain, A. K., & Chandrasekaran, B. (1982) · Handbook of Statistics · doi:10.1016/s0169-7161(82)02042-2 · Cited by 560
Considerations of sample and feature size
Foley, D. (1972) · IEEE Transactions on Information Theory · doi:10.1109/tit.1972.1054863 · Cited by 292
Raudys, Š., & Pikelis, V. (1980) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1980.4767011 · Cited by 167
Cited by
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Murthy, S. K. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009744630224 · Cited by 991
Machine Learning Testing: Survey, Landscapes and Horizons
Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897
Deep Learning in Medical Image Analysis
Chan, H.-P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020) · Advances in Experimental Medicine and Biology · doi:10.1007/978-3-030-33128-3_1 · Cited by 747
Artificial convolution neural network techniques and applications for lung nodule detection
Lo, S.-C. B., Lou, S.-L. A., Lin, J.-S., Freedman, M. T., Chien, M. V., & Mun, S. K. (1995) · IEEE Transactions on Medical Imaging · doi:10.1109/42.476112 · Cited by 348
Giger, M. L., Chan, H., & Boone, J. (2008) · Medical Physics · doi:10.1118/1.3013555 · Cited by 311
Metadata
- Authors
- Fukunaga, K., & Hayes, R. R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1989
- Volume
- 11
- Issue
- 8
- Pages
- 873-885
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2036255459
- Cited by
- 320
- References
- 7
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- closed
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