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.
| Author(s): | Chapelle, O. |
| Book Title: | AISTATS 2005 |
| Journal: | AI STATS |
| Pages: | 49-56 |
| Year: | 2005 |
| Month: | January |
| Day: | 0 |
| Editors: | Cowell, R. , Z. Ghahramani |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics 2005) |
| Event Place: | Barbados |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 0-9727358-1-X |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{2563,
title = {Active Learning for Parzen Window Classifier},
journal = {AI STATS},
booktitle = {AISTATS 2005},
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.},
pages = {49-56},
editors = {Cowell, R. , Z. Ghahramani},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jan,
year = {2005},
slug = {2563},
author = {Chapelle, O.},
month_numeric = {1}
}