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Creators/Authors contains: "Young, Carol"

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  1. ; ; (, 2019 IEEE 58th Conference on Decision and Control (CDC))
    Full Text Available 
  2. ; ; (, International Journal of Robust and Nonlinear Control)
    Summary Expert based ensemble learning algorithms often serve as online learning algorithms for an unknown, possibly time‐varying, probability distribution. Their simplicity allows flexibility in design choices, leading to variations that balance adaptiveness and consistency. This article provides an analytical framework to quantify the adaptiveness and consistency of expert based ensemble learning algorithms. With properly selected states, the algorithms are modeled as a Markov chains. Then quantitative metrics of adaptiveness and consistency can be calculated through mathematical formulas, other than relying on numerical simulations. Results are derived for several popular ensemble learning algorithms. Success of the method has also been demonstrated in both simulation and experimental results. 
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