Combining Multiple Learners: Data Fusion and Ensemble Learning

Ke-Lin Du () and M. N. S. Swamy
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Ke-Lin Du: Concordia University, Department of Electrical and Computer Engineering
M. N. S. Swamy: Concordia University, Department of Electrical and Computer Engineering

Chapter Chapter 25 in Neural Networks and Statistical Learning, 2019, pp 737-767 from Springer

Abstract: 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.

Date: 2019
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DOI: 10.1007/978-1-4471-7452-3_25

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