Knowledge Acquisition form Examples Vis Multiple Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Interpreting Models via Single Tree Approximation (2016, background): “This idea is related to Domingos [1997] in which the author introduced Combined Multiple Models(CMM) to learn a base learner from a multiple model that is complex, incomprehensible but accurate.”
Occam's Two Razors: The Sharp and the Blunt (1998, background): “In particular , Rao and Potts (1997) show how bagging builds accurate frontiers from CART trees that approximate them poorly, and Domingos (1997b) shows how a single model learned by emulating the behavior of a bagged ensemble is both more complex and more accurate than a model induced directly…”
A Survey of Methods for Scaling Up Inductive Algorithms (1999, background): “A quite different approach to creating comprehensible classi-ﬁers from ensembles is taken by Craven (1996), by Domingos (1997), and by Guo and Sutiwaraphun (1998).”
Tree Induction for Probability-Based Ranking (2003, methodology): “4 tree may bepreferable, or a method like CMM (Domingos, 1997) (whi h produ esa single-tree approximation of the ensemble) may be useful.8.”
A Perspective View and Survey of Meta-Learning (2002, background): “Domingos (1997, 1998) shows empirical evidence supporting the claim that a meta-learner can improve the accuracy of base-learners while retaining comprehensibility.”