libagf iconlibagf 0.9.5

libagf is a innovative implementation of adaptive or variable-bandwidth kernel-based estimators for statistical classification.
libagf (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 classification algorithms in the world. These include:

matching kernel width to sample density quickly and accurately via the properties of the exponential function
restricting calculations to a set of k-nearest-neighbours found in n log k time with a binary tree
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

What's New in This Release:

Finished the QUICKSTART file. This contains (or should contain) everything the beginning user needs to get up and running with the package.
Also added the paper describing the theory of Adaptive Gaussian Filtering.
Changed the name of the whole repository to libagf, same as project name.

last updated on:
September 15th, 2012, 9:16 GMT
price:
FREE!
developed by:
Peter Mills
license type:
GPL (GNU General Public License) 
category:
ROOT \ Science and Engineering \ Mathematics

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libagf
What's New in This Release:
  • The k-nearest-neighbours routine is now based on a quicksort instead of a binary tree, and a weights calculation routine implemented that solves for the filter variance using the same root-finding algorithm (supernewton) as the class borders algorithm. To accommodate these changes, several options have been changed/added.
read full changelog

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