Adaptiveness and consistency of a class of online ensemble learning algorithms

Abstract

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 28, 2020
Source ID
10.1002/rnc.5292

Entities

People

  • Carol Young
  • Fumin Zhang
  • Ningshi Yao

Organizations

  • Air Force Office of Scientific Research
  • Georgia Tech
  • National Oceanic and Atmospheric Administration
  • National Science Foundation
  • Office of Naval Research
  • Sandia National Laboratories
  • United States Naval Research Laboratory

Tags

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design