Learning from Randomly-Distributed Inaccurate Measurements
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.
KeywordsMeasurable Subset Statistical Learn Theory Hypothesis Space Confidence Probability Fixed Radius
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