Combining Multiple Learners: Data Fusion and Emsemble Learning



Different learning algorithms have different accuracies. The no free lunch theorem asserts that no single learning algorithm always achieves the best performance in any domain. They can be combined to attain higher accuracy. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation. Fusion of data for improving prediction accuracy and reliability is an important problem in machine learning.


Random Forest Decision Function Ensemble Method Variance Decomposition Belief Function 
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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Enjoyor LabsEnjoyor Inc.HangzhouChina
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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