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
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Hierarchical Multiclassifier System for Hyperspectral Data Analysis
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Stabilizing Classifiers for Very Small Sample Sizes
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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Classification of hyperspectral data is challenging because of high dimensionality (O(100)) inputs, several possible output classes with uneven priors, and scarcity of labeled information. In an earlier work, a multiclassifier system arranged as a binary hierarchy was developed to group classes for easier, progressive discrimination [27]. This paper substantially expands the scope of such a system by integrating a feature reduction scheme that adaptively adjusts to the amount of labeled data available, while exploiting the highly correlated nature of certain adjacent hyperspectral bands. The resulting best-basis binary hierarchical classifier (BB-BHC) family is thus able to address the "small sample size" problem, as evidenced by our experimental results.