Stack filter classifiers

Porter, Reid B; Hush, Don

doi:10.1007/978-3-642-03613-2_26

Stack filter classifiers

Conference · Wed Dec 31 23:00:00 EST 2008

DOI:https://doi.org/10.1007/978-3-642-03613-2_26· OSTI ID:956351

Porter, Reid B ^[1]; Hush, Don ^[1]

Los Alamos National Laboratory

Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

Research Organization:: Los Alamos National Laboratory (LANL)

Sponsoring Organization:: DOE

DOE Contract Number:: AC52-06NA25396

OSTI ID:: 956351

Report Number(s):: LA-UR-09-00530; LA-UR-09-530

Country of Publication:: United States

Language:: English

References (8)

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Related Subjects

99 GENERAL AND MISCELLANEOUS
ADDITIVES
ALGORITHMS
CLASSIFICATION
COMPUTERS
IMAGE PROCESSING
LEARNING
STATISTICS

Stack filter classifiers

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References (8)

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