Learning algorithms for stack filter classifiers
- Los Alamos National Laboratory
- TEXAS A&M
Stack Filters define a large class of increasing filter that is used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: (1) fast and efficient implementation, (2) the relationship to mathematical morphology and (3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in an earlier paper. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help control estimation and approximation errors, and also suggests several new learning algorithms for Boolean function classifiers when they are applied to real-valued inputs.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 956436
- Report Number(s):
- LA-UR-09-01010; LA-UR-09-1010; TRN: US201013%%162
- Resource Relation:
- Conference: International Symposium on Mathematical Morphology ; August 24, 2009 ; Groningen, The Netherlands
- Country of Publication:
- United States
- Language:
- English
Similar Records
Stacked filters: learning to filter by structure
Error minimizing algorithms for nearest eighbor classifiers