English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Active Learning for Parzen Window Classifier

MPS-Authors
/persons/resource/persons83855

Chapelle,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Chapelle, O. (2005). Active Learning for Parzen Window Classifier. In R. Cowell, & Z. Ghahramani (Eds.), AISTATS 2005: Tenth International Workshop onArtificial Intelligence and Statistics (pp. 49-56). The Society for Artificial Intelligence and Statistics.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D689-2
Abstract
The problem of active learning is approached in this paper by minimizing
directly an estimate of the expected test error. The main difficulty
in this ``optimal'' strategy is that output probabilities need to be
estimated accurately. We suggest here different methods
for estimating those efficiently.
In this context, the Parzen window classifier is considered
because it is both simple and probabilistic. The analysis of experimental
results highlights that regularization is a key ingredient for this strategy.