Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain
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 typica...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/434826 |
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| _version_ | 1832553418859741184 |
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| author | Elena Vildjiounaite Georgy Gimel’farb Vesa Kyllönen Johannes Peltola |
| author_facet | Elena Vildjiounaite Georgy Gimel’farb Vesa Kyllönen Johannes Peltola |
| author_sort | Elena Vildjiounaite |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b07de3813a034dd59fbf68d2c2a3e559 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-b07de3813a034dd59fbf68d2c2a3e5592025-02-03T05:54:00ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/434826434826Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging DomainElena Vildjiounaite0Georgy Gimel’farb1Vesa Kyllönen2Johannes Peltola3VTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, FinlandThe University of Auckland, Private Bag 92019, Auckland 1149, New ZealandVTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, FinlandVTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, FinlandIntelligent 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.http://dx.doi.org/10.1155/2015/434826 |
| spellingShingle | Elena Vildjiounaite Georgy Gimel’farb Vesa Kyllönen Johannes Peltola Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain The Scientific World Journal |
| title | Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain |
| title_full | Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain |
| title_fullStr | Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain |
| title_full_unstemmed | Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain |
| title_short | Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain |
| title_sort | lightweight adaptation of classifiers to users and contexts trends of the emerging domain |
| url | http://dx.doi.org/10.1155/2015/434826 |
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