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Conference papers
LiLoLe—A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling
Abstract : Adaptation in context-aware ubiquitous environments and adaptive systems is becoming more and more complex. Adaptations need to take into account information from a plethora of heterogeneous sensors, while the adaptation decisions often imply personalised aspects and individual preferences, which are likely to change over time. We present a novel concept for lifelong learning from sensor data streams for predictive user modelling that is applicable in scenarios where simpler mechanisms that rely on pre-trained general models fall short. With the LiLoLe-Framework, we pursue an approach that allows ubiquitous systems to continuously learn from their users and adapt the system at the same time through stream-based active learning. This Framework can guide the development of context-aware or adaptive systems in form of an overall architecture.
Cited literature [36 references]
https://hal.inria.fr/hal-01405070
Contributor : Hal Ifip <>
Submitted on : Tuesday, November 29, 2016 - 2:50:05 PM
Last modification on : Tuesday, November 29, 2016 - 3:02:05 PM
Long-term archiving on: : Monday, March 27, 2017 - 8:44:09 AM
Contributor : Hal Ifip <>
Submitted on : Tuesday, November 29, 2016 - 2:50:05 PM
Last modification on : Tuesday, November 29, 2016 - 3:02:05 PM
Long-term archiving on: : Monday, March 27, 2017 - 8:44:09 AM
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