T16-PeopleCentricInferencing

T16-PeopleCentricInferencing - Cooperative Techniques...

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Nicholas D. Lane, Hong Lu, Shane B. Eisenman, and Andrew T. Campbell Presenter: Pete Clements Cooperative Techniques Supporting Sensor-based People-centric Inferencing
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Background MetroSense Andrew T. Campbell Collaboration between labs at Dartmouth & Columbia University Projects Include SoundSense CenceMe Sensor Sharing BikeNet AnonySense Second Life Sensor
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Problem People-centric sensor-based applications need models to provide custom experience Learning inference models is hampered by Lack of labeled training data Insufficient training data Disincentive due to time and effort Appropriate feature inputs Heterogeneous devices Insufficient data inputs
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Proposed Solution Opportunistic feature vector merging Social-network-driven sharing of Model training data Models themselves
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Related Work Sharing training sets in machine learning nomenclature known as co-training Several successful systems using
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This note was uploaded on 11/18/2011 for the course EEL 6788 taught by Professor Boloni,l during the Spring '08 term at University of Central Florida.

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T16-PeopleCentricInferencing - Cooperative Techniques...

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