libagf: Adaptive Gaussian filtering (original) (raw)

Next:
Theory

• Sourceforge
• News
• CVS
• Statistics
• Admin
• Download Contact:
  Peter Mills

libagf is a Peteysoft project

Looking for the latest version of this library? Please head over to github:libmsci. 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 For the latest information on this software, including updates, planned improvements and theoretical discussions, please check the Peteysoft homepage or the Notational Shorthandblog.

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