Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition
Summary :
The main problem in statistical pattern recognition is to design a classifier. Many researchers point out that a finite number of training samples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, we compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclidean distance classifiers) when the number of training samples is small.
- Publication
- IEICE TRANSACTIONS on Information Vol.E77-D No.3 pp.355-357
- Publication Date
- 1994/03/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Image Processing, Computer Graphics and Pattern Recognition
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Yoshihiko HAMAMOTO, Shunji UCHIMURA, Shingo TOMITA, "Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition" in IEICE TRANSACTIONS on Information,
vol. E77-D, no. 3, pp. 355-357, March 1994, doi: .
Abstract: The main problem in statistical pattern recognition is to design a classifier. Many researchers point out that a finite number of training samples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, we compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclidean distance classifiers) when the number of training samples is small.
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_3_355/_p
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@ARTICLE{e77-d_3_355,
author={Yoshihiko HAMAMOTO, Shunji UCHIMURA, Shingo TOMITA, },
journal={IEICE TRANSACTIONS on Information},
title={Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition},
year={1994},
volume={E77-D},
number={3},
pages={355-357},
abstract={The main problem in statistical pattern recognition is to design a classifier. Many researchers point out that a finite number of training samples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, we compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclidean distance classifiers) when the number of training samples is small.},
keywords={},
doi={},
ISSN={},
month={March},}
Copy
TY - JOUR
TI - Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 355
EP - 357
AU - Yoshihiko HAMAMOTO
AU - Shunji UCHIMURA
AU - Shingo TOMITA
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 1994
AB - The main problem in statistical pattern recognition is to design a classifier. Many researchers point out that a finite number of training samples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, we compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclidean distance classifiers) when the number of training samples is small.
ER -
English
