Results 221 to 230 of about 147,166 (261)

Learning Importance of Preferences

EPiC Series in Computing, 2018
We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the
Ying Zhu, Mirek Truszczynski
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Learning Preference Inventory

Medical Teacher, 1983
In this series we print evaluation instruments, questionnaires, rating scales and similar resource materials useful to teachers, evaluators and planners. You may wish to adapt such instruments for your own purposes. The feature this issue was developed by Professor A. G.
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U-Processes and Preference Learning

Neural Computation, 2014
Preference learning has caused great attention in machining learning. In this letter we propose a learning framework for pairwise loss based on empirical risk minimization of U-processes via Rademacher complexity. We first establish a uniform version of Bernstein inequality of U-processes of degree 2 via the entropy methods. Then we estimate the bound
Hong Li 0009, Chuanbao Ren, Luoqing Li
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Learning and Optimizing with Preferences

2013
Preferences and choices are a central source of information generated by humans. They have been studied for centuries in the context of social choice theory, econometric theory, statistics and psychology. At least two Nobel prizes in economics have been awarded for work reasoning about human preferences and choices.
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Learning individual mating preferences

Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011
Mate selection is a key step in evolutionary algorithms which traditionally has been panmictic and based solely on fitness. Various mate selection techniques have been published which show improved performance due to the introduction of mate restrictions or the use of genotypic/phenotypic features.
Lisa M. Guntly, Daniel R. Tauritz
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Learning from revealed preference

Proceedings of the 7th ACM conference on Electronic commerce, 2006
A sequence of prices and demands are rationalizable if there exists a concave, continuous and monotone utility function such that the demands are the maximizers of the utility function over the budget set corresponding to the price. Afriat [1] presented necessary and sufficient conditions for a finite sequence to be rationalizable.
Eyal Beigman, Rakesh Vohra
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Collaborative learning of preference rankings

Proceedings of the sixth ACM conference on Recommender systems, 2012
We propose a model for learning user preference rankings for the purpose of making product recommendations. The model allows us to learn from pairwise preference statements or from (incomplete) rankings over more than two items. We present two algorithms for performing inference in this model, both with excellent scaling in the number of users and ...
Salimans, T (Tim), Paquet, U, Graepel, T
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On learning of weights through preferences

Information Sciences, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Preferred Learning Style Index

Medical Teacher, 1979
In this occasional series, we will be printing evaluation instruments, questionnaires, rating scales and similar resource materials useful to teachers, evaluators and planners.
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Chemical Transfer of Learned Preference

Nature, 1969
IT has been reported1–2 that the natural preference of mice for the dark can be reversed by injection of brain extracts taken from donors trained to avoid a black box. This effect was considered to represent a "transfer of learned fear". The possibility remained, however, that the apparent transfer was caused by a substance produced in response to the ...
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