NDLI: Semiparametric preference learning

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Semiparametric preference learning

Content Provider	IEEE Xplore Digital Library
Author	Zhen, Yi Song, Yangqiu Yeung, Dit-Yan
Copyright Year	1996
Abstract	Unlike traditional supervised learning problems, preference learning learns from data available in the form of pairwise preference relations between instances. Existing preference learning methods are either parametric or nonparametric in nature. We propose in this paper a semiparametric preference learning model, abbreviated as SPPL, with the aim of combining the strengths of the parametric and nonparametric approaches. SPPL uses multiple Gaussian processes which are linearly coupled to determine the preference relations between instances. SPPL is more powerful than previous models while keeping the computational complexity low (linear in the number of distinct instances). We devise an efficient algorithm for model learning. Empirical studies have been conducted on two real-world data sets showing that SPPL outperforms related preference learning methods.
Starting Page	257
Ending Page	264
Page Count	8
File Size	1500209
File Format	PDF
ISSN	10070214
Volume Number	19
Issue Number	3
Language	English
Publisher	Tsinghua University Press
Publisher Date	2014-01-01
Access Restriction	Subscribed
Rights Holder	Tsinghua University Press
Subject Keyword	Gaussian process semiparametric learning preference learning
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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