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Sato, Taisuke ; Kameya, Yoshitaka

Learning through failure

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


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

  author =	{Taisuke Sato and Yoshitaka Kameya},
  title =	{Learning through failure},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  year =	{2006},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor  and Stephen H. Muggleton},
  number =	{05051},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{},
  annote =	{Keywords: Program transformation, failure, generative modeling}

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

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