Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bootstrap Technique for Nearest Neighbor Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning When Negative Examples Abound
ECML '97 Proceedings of the 9th European Conference on Machine Learning
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In this paper, we discuss the improvement of the generalization ability of Parzen classifiers, in small sample, high-dimensional setting. When the sizes of samples per class are much unequal, the performance of the Parzen classifier is further degraded. Also, in a high-dimensional space, the degradation becomes clear. In order to overcome this problem, we propose to use the Toeplitz estimator and bootstrap samples in designing Parzen classifiers. Experimental results show that these two techniques are very effective means for designing Parzen classifiers, particularly when the sizes of samples per class are much unequal, or when the number of features is large.