Learning from Randomly-Distributed Inaccurate Measurements

  • John Eidson
  • Bruce Hamilton
  • Valery Kanevsky


Traditional measurement systems are designed with tight control over the time and place of measurement of the device or environment under test. This is true whether the measurement system uses a centralized or a distributed architecture. Currently there is considerable interest in using mobile consumer devices as measurement platforms for testing large dispersed systems. There is also growing activity in developing concepts of ubiquitous measurement, such as “smart dust.” Under these conditions the times and places of measurement are random, which raises the question of the validity and interpretation of the acquired data. This paper presents a mathematical analysis that shows it is possible under certain conditions to establish dependence between error bounds and confidence probability on models built using data acquired in this manner.


Measurable Subset Statistical Learn Theory Hypothesis Space Confidence Probability Fixed Radius 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • John Eidson
    • 1
  • Bruce Hamilton
    • 1
  • Valery Kanevsky
    • 1
  1. 1.Agilent TechnologiesUSA

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