Active Learning of Label Ranking Functions
Abstract
The effort necessary to construct labeled sets of examples in a supervisedlearning scenario is often disregarded, though in many applications, it is atime-consuming and expensive procedure. While this already constitutes a majorissue in classification learning, it becomes an even more serious problem whendealing with the more complex target domain of total orders over a set ofalternatives. Considering both the pairwise decomposition and the constraintclassification technique to represent label ranking functions, we introduce anovel generalization of pool-based active learning to address this problem.
Cite
Text
Brinker. "Active Learning of Label Ranking Functions." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015331Markdown
[Brinker. "Active Learning of Label Ranking Functions." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/brinker2004icml-active/) doi:10.1145/1015330.1015331BibTeX
@inproceedings{brinker2004icml-active,
title = {{Active Learning of Label Ranking Functions}},
author = {Brinker, Klaus},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015331},
url = {https://mlanthology.org/icml/2004/brinker2004icml-active/}
}