Preference Inference through Rescaling Preference Learning
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2203-2208.
Abstract
One approach to preference learning, based on linear support vector machines, involves choosing a weight vector whose associated hyperplane has maximum margin with respect to an input set of preference vectors, and using this to compare feature vectors. However, as is well known, the result can be sensitive to how each feature is scaled, so that rescaling can lead to an essentially different vector. This gives rise to a set of possible weight vectors - which we call the rescale-optimal ones - considering all possible rescalings. From this set one can define a more cautious preference relation, in which one vector is preferred to another if it is preferred for all rescale-optimal weight vectors. In this paper, we analyse which vectors are rescale-optimal, and when there is a unique rescale-optimal vector, and we consider how to compute the induced preference relation.
@inproceedings{ijcai2016p314,
title = {Preference Inference through Rescaling Preference Learning},
author = {Wilson, Nic and Montazery, Mojtaba},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2203--2208},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/314},
}
TY - CPAPER TI - Preference Inference through Rescaling Preference Learning AU - Wilson, Nic AU - Montazery, Mojtaba T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2203 EP - 2208 UR - https://ijcai.org/proceedings/2016/314 ER -Copied
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