Analysis of Perceptron-Based Active Learning
Abstract
We start by showing that in an active learning setting, the Perceptron algorithm needs $\Omega(\frac{1}{\epsilon ^{2}})$ labels to learn linear separators within generalization error ε . We then present a simple selective sampling algorithm for this problem, which combines a modification of the perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization error ε after asking for just ${\tilde O}(d log \frac{1}{\epsilon})$ labels. This exponential improvement over the usual sample complexity of supervised learning has previously been demonstrated only for the computationally more complex query-by-committee algorithm.
Cite
Text
Dasgupta et al. "Analysis of Perceptron-Based Active Learning." Annual Conference on Computational Learning Theory, 2005. doi:10.1007/11503415_17Markdown
[Dasgupta et al. "Analysis of Perceptron-Based Active Learning." Annual Conference on Computational Learning Theory, 2005.](https://mlanthology.org/colt/2005/dasgupta2005colt-analysis/) doi:10.1007/11503415_17BibTeX
@inproceedings{dasgupta2005colt-analysis,
title = {{Analysis of Perceptron-Based Active Learning}},
author = {Dasgupta, Sanjoy and Kalai, Adam Tauman and Monteleoni, Claire},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2005},
pages = {249-263},
doi = {10.1007/11503415_17},
url = {https://mlanthology.org/colt/2005/dasgupta2005colt-analysis/}
}