Intrinsic dimensionality and small sample properties of classifiers
Kybernetika (1998)
- Volume: 34, Issue: 4, page [461]-466
- ISSN: 0023-5954
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topRaudys, Šarūnas. "Intrinsic dimensionality and small sample properties of classifiers." Kybernetika 34.4 (1998): [461]-466. <http://eudml.org/doc/33378>.
@article{Raudys1998,
abstract = {Small learning-set properties of the Euclidean distance, the Parzen window, the minimum empirical error and the nonlinear single layer perceptron classifiers depend on an “intrinsic dimensionality” of the data, however the Fisher linear discriminant function is sensitive to all dimensions. There is no unique definition of the “intrinsic dimensionality”. The dimensionality of the subspace where the data points are situated is not a sufficient definition of the “intrinsic dimensionality”. An exact definition depends both, on a true distribution of the pattern classes, and on the type of the classifier used.},
author = {Raudys, Šarūnas},
journal = {Kybernetika},
keywords = {intrinsic dimensionality; nonlinear classifiers; intrinsic dimensionality; nonlinear classifiers},
language = {eng},
number = {4},
pages = {[461]-466},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Intrinsic dimensionality and small sample properties of classifiers},
url = {http://eudml.org/doc/33378},
volume = {34},
year = {1998},
}
TY - JOUR
AU - Raudys, Šarūnas
TI - Intrinsic dimensionality and small sample properties of classifiers
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 4
SP - [461]
EP - 466
AB - Small learning-set properties of the Euclidean distance, the Parzen window, the minimum empirical error and the nonlinear single layer perceptron classifiers depend on an “intrinsic dimensionality” of the data, however the Fisher linear discriminant function is sensitive to all dimensions. There is no unique definition of the “intrinsic dimensionality”. The dimensionality of the subspace where the data points are situated is not a sufficient definition of the “intrinsic dimensionality”. An exact definition depends both, on a true distribution of the pattern classes, and on the type of the classifier used.
LA - eng
KW - intrinsic dimensionality; nonlinear classifiers; intrinsic dimensionality; nonlinear classifiers
UR - http://eudml.org/doc/33378
ER -
References
top- Duin R. P. W., Superlearning capabilities of neural networks, In: Proc. of the 8th Scandinavian Conference on Image Analysis NOVIM, Norwegian Society for Image Processing and Pattern Recognition, Tromso 1993, pp. 547–554 (1993)
- Raudys Š., Linear classifiers in perceptron design, In: Proceedings 13th ICPR, Vol. 4, Track D, Vienna 1996, IEEE Computer Society Press, Los Alamitos, pp. 763–767 (1996)
- Raudys Š., On dimensionality, sample size and classification error of nonparametric linear classification algorithms, IEEE Trans. Pattern Analysis Machine Intelligence PAMI-19 (1989), 6, 669–671 (1989)
- Raudys Š., Jain A. K., 10.1109/34.75512, IEEE Trans. Pattern Analysis Machine Intelligence PAMI-13 (1991), 252–264 (1991) DOI10.1109/34.75512
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