NDLI: Efficiently learning the preferences of people

Content Provider	Springer Nature Link
Author	Birlutiu, Adriana Groot, Perry Heskes, Tom
Copyright Year	2012
Abstract	This paper presents a framework for optimizing the preference learning process. In many real-world applications in which preference learning is involved the available training data is scarce and obtaining labeled training data is expensive. Fortunately in many of the preference learning situations data is available from multiple subjects. We use the multi-task formalism to enhance the individual training data by making use of the preference information learned from other subjects. Furthermore, since obtaining labels is expensive, we optimally choose which data to ask a subject for labelling to obtain the most of information about her/his preferences. This paradigm—called active learning—has hardly been studied in a multi-task formalism. We propose an alternative for the standard criteria in active learning which actively chooses queries by making use of the available preference data from other subjects. The advantage of this alternative is the reduced computation costs and reduced time subjects are involved. We validate empirically our approach on three real-world data sets involving the preferences of people.
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	90
Issue Number	1
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2012-05-31
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning preferences Active learning Experimental design Multi-task learning Hierarchical modeling Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Computing Methodologies Simulation and Modeling Language Translation and Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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