Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1358-1363.
Abstract
We propose a multi-prototype-based algorithm for online learning of soft pairwise-preferences over labels. The algorithm learns soft label preferences via minimization of the proposed soft rank-loss measure, and can learn from total orders as well as from various types of partial orders. The soft pairwise preference algorithm outputs are further aggregated to produce a total label ranking prediction using a novel aggregation algorithm that outperforms existing aggregation solutions. Experiments on synthetic and real-world data demonstrate state-of-the-art performance of the proposed model.
@inproceedings{ijcai2013p204,
title = {Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation},
author = {Grbovic, Mihajlo and Djuric, Nemanja and Vucetic, Slobodan},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1358--1363},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/204},
}
TY - CPAPER TI - Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation AU - Grbovic, Mihajlo AU - Djuric, Nemanja AU - Vucetic, Slobodan T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1358 EP - 1363 UR - https://ijcai.org/proceedings/2013/204 ER -Copied
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