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The Scientific World Journal
Volume 2015, Article ID 434826, 29 pages
Review Article

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

1VTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, Finland
2The University of Auckland, Private Bag 92019, Auckland 1149, New Zealand

Received 27 April 2015; Revised 10 July 2015; Accepted 22 July 2015

Academic Editor: Stefanos Kollias

Copyright © 2015 Elena Vildjiounaite et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.