This page contains resources about Ensemble Learning.


  • Bayesian Averaging
    • Reversible jump MCMC
    • Birth and death MCMC
    • Bayesian Model Combination
  • Bagging
  • Boosting
  • Bayes Optimal Classifier
  • Decision Forests / Random Forests
  • Stacking
  • Committee Machines
  • Mixture Models
    • Mixture of Experts
    • k-Nearest Neighbour (k-NN)
    • Mixture of Factor Analyzers

Online CoursesEdit

Video LecturesEdit

Lecture NotesEdit

Books and Book ChaptersEdit

  • Zhou, Z. H. (2015). "Ensemble Learning". Encyclopedia of biometrics. Springer.
  • Zhou, Z. H. (2012). Ensemble methods: foundations and algorithms. CRC press.
  • Alpaydin, E. (2010). "Chapter 17: Combining Multiple Learners". Introduction to machine learning. MIT Press.
  • Russell, S. J., & Norvig, P. (2010). "Section 18.10: Ensemble Learning". Artificial Intelligence: A Modern Approach. Prentice Hall.
  • Bishop, C. M. (2006). "Chapter 9: Mixture Models and EM". Pattern Recognition and Machine Learning. Springer.
  • Bishop, C. M. (2006). "Chapter 14: Combining Models". Pattern Recognition and Machine Learning. Springer.
  • Kuncheva, L. I. (2004). Combining pattern classifiers: methods and algorithms. John Wiley & Sons.
  • Dietterich, T. G. (2002). "Ensemble Learning". The handbook of brain theory and neural networks. MIT Press.

Scholarly ArticlesEdit



See alsoEdit

Other ResourcesEdit

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