DagSemProc.05051.9.pdf
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Learning through failure
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Dagstuhl Seminar Proceedings, Volume 5051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-01-19
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Taisuke Sato and Yoshitaka Kameya. Learning through failure. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)
https://doi.org/10.4230/DagSemProc.05051.9
https://doi.org/10.4230/DagSemProc.05051.9
Abstract
PRISM, a symbolic-statistical modeling language
we have been developing since '97, recently
incorporated a program transformation technique
to handle failure in generative modeling.
I'll show this feature opens a way to
new breeds of symbolic models, including
EM learning from negative observations,
constrained HMMs and finite PCFGs.
Keywords
- Program transformation
- failure
- generative modeling
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