%0 Conference Proceedings
%T LiLoLe—A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling
%+ University of Bamberg
%A Fetter, Mirko
%A Gross, Tom
%Z Part 1: Research Papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 5th International Conference on Human-Centred Software Engineering (HCSE)
%C Paderborn, Germany
%Y Stefan Sauer
%Y Cristian Bogdan
%Y Peter Forbrig
%Y Regina Bernhaupt
%Y Marco Winckler
%I Springer
%3 Human-Centered Software Engineering
%V LNCS-8742
%P 126-143
%8 2014-09-16
%D 2014
%R 10.1007/978-3-662-44811-3_8
%K Lifelong Learning
%K User Modelling
%K Framework
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 13
%Z WG 13.2
%2 https://inria.hal.science/hal-01405070/document
%2 https://inria.hal.science/hal-01405070/file/978-3-662-44811-3_8_Chapter.pdf
%L hal-01405070
%U https://inria.hal.science/hal-01405070
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-LNCS-8742
%~ IFIP-TC13
%~ IFIP-HCSE
%~ IFIP-WG13-2