ICML 2017poster52 citations
Logarithmic Time One-Against-Some
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
Abstract
We create a new online reduction of multiclass classification to binary classification for which training and prediction time scale logarithmically with the number of classes. We show that several simple techniques give rise to an algorithm which is superior to previous logarithmic time classification approaches while competing with one-against-all in space. The core construction is based on using a tree to select a small subset of labels with high recall, which are then scored using a one-against-some structure with high precision.
BibTeX
@InProceedings{pmlr-v70-daume17a,
title = {Logarithmic Time One-Against-Some},
author = {Daum{\'e}, III, Hal and Nikos Karampatziakis and John Langford and Paul Mineiro},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {923--932},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/daume17a/daume17a.pdf},
url = {https://proceedings.mlr.press/v70/daume17a.html},
abstract = {We create a new online reduction of multiclass classification to binary classification for which training and prediction time scale logarithmically with the number of classes. We show that several simple techniques give rise to an algorithm which is superior to previous logarithmic time classification approaches while competing with one-against-all in space. The core construction is based on using a tree to select a small subset of labels with high recall, which are then scored using a one-against-some structure with high precision.}
}