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

Vildjiounaite E, Gimel'farb G, Kyllönen V, Peltola J - ScientificWorldJournal (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: VTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, Finland.

ABSTRACT
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.

No MeSH data available.


Lightweight adaptation summary (Section 4).
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig3: Lightweight adaptation summary (Section 4).

Mentions: Adaptation time: that is, whether the adaptation must be very quick (e.g., the interface should be adapted just at the moment of launching an application) or can take time or whether a lifelong learning is expected.


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

Vildjiounaite E, Gimel'farb G, Kyllönen V, Peltola J - ScientificWorldJournal (2015)

Lightweight adaptation summary (Section 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4581555&req=5

fig3: Lightweight adaptation summary (Section 4).
Mentions: Adaptation time: that is, whether the adaptation must be very quick (e.g., the interface should be adapted just at the moment of launching an application) or can take time or whether a lifelong learning is expected.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: VTT Technical Research Centre of Finland, Kaitoväylä 1, 90571 Oulu, Finland.

ABSTRACT
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.

No MeSH data available.