article · 2000
Ensemble Learning
Harri Lappalainen, James W. Miskin
Cited by 0Abstract and details only
Abstract
Introduction When we say we are making a model of a system, we are setting up a tool which can be used to make inferences, predictions and decisions. Each model can be seen as a hypothesis, or explanation, which makes assertions about the quantities which are directly observable and which can only be inferred from their effect on observable quantities. In the Bayesian framework, knowledge is contained in the conditional probability distributions of the models. We can use Bayes' theorem to evaluate the conditional probability distributions for the unknown parameters, y, given the set of observed quantities, x, using p (y jx ) = p (x jy ) p (y) p (x) (1) The prior distribution p (y) contains our knowledge of the unknown variables before we make any obser
Cite this paper
Lappalainen, H., & Miskin, J. W. (2000). Ensemble Learning.
Lappalainen, Harri, and James W. Miskin. “Ensemble Learning.” 2000.
Lappalainen, Harri, and James W. Miskin. 2000. “Ensemble Learning.”
Lappalainen, H. and Miskin, J.W. (2000) ‘Ensemble Learning’.
@article{lappalainen2000ensemble,
title = {{Ensemble Learning}},
author = {Lappalainen, Harri and Miskin, James W.},
year = {2000}
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