MSR logo

pKNN+AL: Probabilistic Nearest Neighbor Classifier with Active Learning

About | Downloads | Documentation | Contact

Latest version: 1.0

About pKNN+AL

pKNN+AL is a Matlab implementation of Probabilistic Nearest Neighbor Classifier algorithm. pKNN+AL also provides routines for active selection of the examples to be labeled. pKNN uses kernel density estimation to specify probability of a point belonging to a particular class. Using the provided class information, the underlying kernel function can be learned using a modification of Bregman's cyclic projection algorithm. Active selection can be performed through a variety of heuristics that use the computed class probability distribution for each unlabeled point.

This software is currently being actively maintained; please check back often for updates. When using this code, please cite pKNN+AL and the relevant paper.

  author    = {Prateek Jain and
               Ashish Kapoor}
  title     = {Active Learning for Large Multi-class Problems},
  booktitle = {CVPR},
  year      = {2009},
  address   = {Miami, Florida, USA}
  month     = {June}

  title        = {Probabilistic Nearest Neighbor Classifier with Active Learning},
  author       = {Prateek Jain and Ashish Kapoor},
  organization = {Microsoft Research, Redmond},
  address      = {},


Source code

Download the latest version (1.0, released 2009-8-05):


README: How to install and use pKNN+AL.


If you have any questions, suggestions, or bug reports about this implementation, please contact Prateek Jain (pjain at cs dot utexas dot edu) and Ashish Kapoor (akapoor at microsoft dot com)