Learning through failure

Authors Taisuke Sato, Yoshitaka Kameya



PDF
Thumbnail PDF

File

DagSemProc.05051.9.pdf
  • Filesize: 150 kB
  • 6 pages

Document Identifiers

Author Details

Taisuke Sato
Yoshitaka Kameya

Cite As Get BibTex

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

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.

Subject Classification

Keywords
  • Program transformation
  • failure
  • generative modeling

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail