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- W148962823 abstract "This chapter focuses on multi-view learning, incremental learning and meta-learning, highly relevant yet understudied machine learning principles that enable life-long learning in context-aware mobile applications. The authors present a Bayesian framework to realize them in modern ubiquitous computing environments that are characterized by dynamic and ever-evolving contexts inferred from heterogeneous sensors with varying churn rates. These techniques enable life-long learning in the context-aware applications to meta-learn their learning principles and continuously adapt the context models in-tune with their environments. This chapter studies the benefits of the proposed techniques and demonstrate their advantages for context-aware mobile applications." @default.
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- W148962823 date "2014-01-01" @default.
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- W148962823 title "A Bayesian Framework for Life-Long Learning in Context-Aware Mobile Applications" @default.
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- W148962823 doi "https://doi.org/10.1007/978-1-4939-1887-4_9" @default.
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