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- W1984858032 abstract "Activity Recognition is an integral component of ubiquitous computing. Recognizing an activity is a challenging task since activities can be concurrent, interleaved or ambiguous and can consist of multiple actors (which would require parallel activity recognition). This paper investigates how the discriminative nature of Conditional Random Fields (CRF) can be exploited to enhance the accuracy of recognizing activities when compared to that achieved using generative models. It aims to apply CRF to recognize complex activities, analyze the model trained by CRF and evaluate the performance of CRF against existing models using Stochastic Gradient Descent (which is suitable for online learning)." @default.
- W1984858032 created "2016-06-24" @default.
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- W1984858032 date "2015-06-25" @default.
- W1984858032 modified "2023-10-16" @default.
- W1984858032 title "Activity recognition using conditional random field" @default.
- W1984858032 cites W2103388129 @default.
- W1984858032 doi "https://doi.org/10.1145/2790044.2790045" @default.
- W1984858032 hasPublicationYear "2015" @default.
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