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The dynamic nature of Intelligent Environments (IE’s) present a challenging problem when attempting to model or learn a model of such environments. By their very nature, IE’s are infused with ...
The dynamic nature of Intelligent Environments (IE’s) present a challenging problem when attempting to model or learn a model of such environments. By their very nature, IE’s are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. As a result of this, the quantity, type and availability of data to model such environments is a major issue. Each situation is contextually different and constantly changing. To model each application, training data must be gained that is within the same feature space and has the same distribution, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of target labelled data occurs. It is within these situations that our study is focussed. Within this research we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a Fuzzy Transfer Learning process.
This presentation is taken from the winning entry at the Leicester British Computer Society Postgraduate Research Competition 2012.
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