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Theory
Contact:
Peter Mills
libagf is a Peteysoft project
Adaptive Gaussian Filtering
Adaptive Gaussian Filtering is a simple and powerful implementation of
variable bandwidth kernel estimators for classification, PDF estimation
and interpolation. The library incorporates several innovations to produce
one of the fastest and most accurate supervised statistical classification algorithms
in the world. These include:
- matching kernel width to sample density quickly and accurately
- restricting calculations to a set of k-nearest-neighbours found in O(n) time
- generating a pre-trained model by searching for the class-borders with guaranteed, superlinear convergence
- extrapolating the conditional probabilities to provide solid knowledge of estimate accuracy
Author: Peter Mills
To cite this work:
- Peter Mills (2011) "Efficient statistical classification of satellite measurements." International Journal of Remote Sensing, 32 (21): 6109-6132. doi: 10.1080/01431161.2010.507795.
Next: Theory