Probabilistic explanation based learning
Luc De Raedt, Angelika Kimmig and Hannu Toivonen
In: 18th European Conference on Machine Learning (ECML), Sep 2007, Warsaw, Poland.
Explanation based learning produces generalized explanations
from examples. These explanations are typically built in a deductive
manner and they aim to capture the essential characteristics of the
Probabilistic explanation based learning extends this idea to probabilistic
logic representations, which have recently become popular within the
field of statistical relational learning. The task is now to find the most
likely explanation why one (or more) example(s) satisfy a given concept.
These probabilistic and generalized explanations can then be used to discover
similar examples and to reason by analogy. So, whereas traditional
explanation based learning is typically used for speed-up learning, probabilistic
explanation based learning is used for discovering new knowledge.
Probabilistic explanation based learning has been implemented in a recently
proposed probabilistic logic called ProbLog, and it has been applied
to a challenging application in discovering relationships of interest
in large biological networks.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Hannu Toivonen|
|Deposited On:||08 March 2010|