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Combining Multiple Learners: Data Fusion and Ensemble Learning

In: Neural Networks and Statistical Learning

Author

Listed:
  • Ke-Lin Du

    (Concordia University, Department of Electrical and Computer Engineering
    Xonlink Inc.)

  • M. N. S. Swamy

    (Concordia University, Department of Electrical and Computer Engineering)

Abstract

According to no-free-lunch theorem, there is no single method that always performs the best in any domain. In practice, many methods are available for solving a given problem, each having its limitations. A popular way of dealing with difficult problems is via brainstorming in which participants share their knowledge from different viewpoints, and collective wisdom is achieved by voting on the decision. Data fusion is a concept that combines the results of all these individual methods using ensemble learning. This chapter deals with ensemble learning.

Suggested Citation

  • Ke-Lin Du & M. N. S. Swamy, 2019. "Combining Multiple Learners: Data Fusion and Ensemble Learning," Springer Books, in: Neural Networks and Statistical Learning, edition 2, chapter 0, pages 737-767, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4471-7452-3_25
    DOI: 10.1007/978-1-4471-7452-3_25
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