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Abstract: In many optimization and decision problems the objective function can be expressed as a linear combination of competing criteria, the weights of which specify the relative importance of the criteria for the user. We consider the problem of learning such a "subjective" function from preference judgments collected from traces of user interactions. We propose a new algorithm for that task based on the theory of Support Vector Machines. One advantage of the algorithm is that prior... (Update)
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BibTeX entry: (Update)
Fiechter, C.N., Rogers, S.: Learning Subjective Functions with Large Margins. Proceedings of the Seventeenth ICML. Morgan Kaufmann, (2000) 287-294 http://citeseer.ist.psu.edu/fiechter00learning.html More
@inproceedings{ fiechter00learning,
author = "Claude-Nicolas Fiechter and Seth Rogers",
title = "Learning Subjective Functions with Large Margins",
booktitle = "Proc. 17th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "287--294",
year = "2000",
url = "citeseer.ist.psu.edu/fiechter00learning.html" }
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