License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05051.9
URN: urn:nbn:de:0030-drops-4185
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/418/
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Sato, Taisuke ; Kameya, Yoshitaka

Learning through failure

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05051.SatoTaisuke.ExtAbstract.418.pdf (0.1 MB)

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.

BibTeX - Entry

@InProceedings{sato_et_al:DagSemProc.05051.9,
  author =	{Sato, Taisuke and Kameya, Yoshitaka},
  title =	{{Learning through failure}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2006/418},
  URN =		{urn:nbn:de:0030-drops-4185},
  doi =		{10.4230/DagSemProc.05051.9},
  annote =	{Keywords: Program transformation, failure, generative modeling}
}

Keywords: Program transformation, failure, generative modeling
Collection: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue Date: 2006
Date of publication: 19.01.2006

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