Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):480-488, 2013.
Abstract
We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.
@InProceedings{pmlr-v28-gonen13,
title = {Efficient Active Learning of Halfspaces: an Aggressive Approach},
author = {Alon Gonen and Sivan Sabato and Shai Shalev-Shwartz},
booktitle = {Proceedings of the 30th International Conference on Machine Learning},
pages = {480--488},
year = {2013},
editor = {Sanjoy Dasgupta and David McAllester},
volume = {28},
number = {1},
series = {Proceedings of Machine Learning Research},
address = {Atlanta, Georgia, USA},
month = {17--19 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v28/gonen13.pdf},
url = {http://proceedings.mlr.press/v28/gonen13.html},
abstract = {We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings. }
}
%0 Conference Paper
%T Efficient Active Learning of Halfspaces: an Aggressive Approach
%A Alon Gonen
%A Sivan Sabato
%A Shai Shalev-Shwartz
%B Proceedings of the 30th International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2013
%E Sanjoy Dasgupta
%E David McAllester
%F pmlr-v28-gonen13
%I PMLR
%J Proceedings of Machine Learning Research
%P 480--488
%U http://proceedings.mlr.press
%V 28
%N 1
%W PMLR
%X We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.
TY - CPAPER
TI - Efficient Active Learning of Halfspaces: an Aggressive Approach
AU - Alon Gonen
AU - Sivan Sabato
AU - Shai Shalev-Shwartz
BT - Proceedings of the 30th International Conference on Machine Learning
PY - 2013/02/13
DA - 2013/02/13
ED - Sanjoy Dasgupta
ED - David McAllester
ID - pmlr-v28-gonen13
PB - PMLR
SP - 480
DP - PMLR
EP - 488
L1 - http://proceedings.mlr.press/v28/gonen13.pdf
UR - http://proceedings.mlr.press/v28/gonen13.html
AB - We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.
ER -
Gonen, A., Sabato, S. & Shalev-Shwartz, S.. (2013). Efficient Active Learning of Halfspaces: an Aggressive Approach. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(1):480-488
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