Near-Optimal Sensor Placements In Gaussian Processes

Near-Optimal Sensor Placements In Gaussian Processes. Theory, efficient algorithms and empirical studies andreas krause krausea@cs.cmu.edu computer science department carnegie mellon university pittsburgh, pa 15213 ajit singh ajit@cs.cmu.edu machine learning department carnegie mellon university pittsburgh, pa 15213 carlos guestrin. We propose a submodular sensing quality function that extends studies from discrete sensor placement to an autonomous sampling scheme where sensing sites must be visited frequently.

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A common strategy is to place sensors at the points of highest entropy (variance) in the gp model. This is beneficial in the slam context, where sensing sites themselves bear uncertainties. Theory, efficient algorithms and empirical studies j.

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When monitoring spatial phenomena, which are often modeled as gaussian processes (gps), choosing sensor locations is a fundamental task. Measurements that could have been used to optimize sensor placements [6].

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We propose a mutual information criteria, and show that it produces better placements. This is beneficial in the slam context, where sensing sites themselves bear uncertainties.

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Theory, efficient algorithms and empirical studies j. When monitoring spatial phenomena, which can often be modeled as gaussian processes (gps), choosing sensor locations is a fundamental task.

Source: www.mdpi.com

Gaussian processes, experimental design, active learning, spatial learning, sensor networks We propose a submodular sensing quality function that extends studies from discrete sensor placement to an autonomous sampling scheme where sensing sites must be visited frequently.

Source: www.researchgate.net

Theory, efficient algorithms and empirical studies andreas krause krausea@cs.cmu.edu computer science department carnegie mellon university pittsburgh, pa 15213 ajit singh ajit@cs.cmu.edu machine learning department carnegie mellon university pittsburgh, pa 15213 carlos guestrin. This is beneficial in the slam context, where sensing sites themselves bear uncertainties.

Theory, Efficient Algorithms And Empirical Studies.

We propose a mutual information criteria, and show that it produces better placements. FInding the k best sensor locations out of a finite subset vof possible A common strategy is to place sensors at the points of highest entropy (variance) in the gp model.

When Monitoring Spatial Phenomena, Which Can Often Be Modeled As Gaussian Processes (Gps), Choosing Sensor Locations Is A Fundamental Task.

Measurements that could have been used to optimize sensor placements [6]. This is beneficial in the slam context, where sensing sites themselves bear uncertainties. , 9 ( february ) ( 2008 ) , pp.

These Models, Based On Gaussian Processes, Allow Us To Avoid Strong Assumptions Previously Made In The Literature.

Theory, efficient algorithms and empirical stu. Carlos guestrin, andreas krause, ajit singh. Theory, efficient algorithms and empirical studies andreas krause krausea@cs.cmu.edu computer science department carnegie mellon university pittsburgh, pa 15213 ajit singh ajit@cs.cmu.edu machine learning department carnegie mellon university pittsburgh, pa 15213 carlos guestrin.

Theory, Efficient Algorithms And Empirical Studies J.

When monitoring spatial phenomena, which can often be modeled as gaussian processes (gps), choosing sensor locations is a fundamental task. Carlos guestrin , computer science department, carnegie mellon university published: We propose a submodular sensing quality function that extends studies from discrete sensor placement to an autonomous sampling scheme where sensing sites must be visited frequently.

When Monitoring Spatial Phenomena, Which Can Often Be Modeled As Gaussian Processes (Gps), Choosing Sensor Locations Is A Fundamental Task.

Theory, efficient algorithms and empirical studies journal of machine learning research, 2008 andreas krause February 2008 · journal of machine learning research andreas krause In spatial statistics this is called sampling design: