JAIR is pleased to announce the publication of the following article, which
readers of this newsgroup may find relevant:
Sato, T. and Kameya, Y. (2001)
"Parameter Learning of Logic Programs for Symbolic-Statistical Modeling",
Volume 15, pages 391-454.
Available in PDF, PostScript and compressed PostScript.
For quick access via your WWW browser, use this URL:
http://www.jair.org/abstracts/sato01a.html
More detailed instructions are below.
Abstract: We propose a logical/mathematical framework for statistical
parameter learning of parameterized logic programs, i.e. definite
clause programs containing probabilistic facts with a parameterized
distribution. It extends the traditional least Herbrand model
semantics in logic programming to distribution semantics, possible
world semantics with a probability distribution which is
unconditionally applicable to arbitrary logic programs including ones
for HMMs, PCFGs and Bayesian networks.
We also propose a new EM algorithm, the graphical EM algorithm, that
runs for a class of parameterized logic programs representing
sequential decision processes where each decision is exclusive and
independent. It runs on a new data structure called support graphs
describing the logical relationship between observations and their
explanations, and learns parameters by computing inside and outside
probability generalized for logic programs.
The complexity analysis shows that when combined with OLDT search for
all explanations for observations, the graphical EM algorithm, despite
its generality, has the same time complexity as existing EM
algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside
algorithm for PCFGs, and the one for singly connected Bayesian
networks that have been developed independently in each research
field. Learning experiments with PCFGs using two corpora of moderate
size indicate that the graphical EM algorithm can significantly
outperform the Inside-Outside algorithm.
The article is available via:
-- comp.ai.jair.papers (also see comp.ai.jair.announce)
-- World Wide Web: The URL for our World Wide Web server is
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For direct access to this article and related files try:
http://www.jair.org/abstracts/sato01a.html
-- Anonymous FTP from Carnegie-Mellon University (USA):
ftp://ftp.cs.cmu.edu/project/jair/volume15/sato01a.ps
The compressed PostScript file is named sato01a.ps.Z (323K)
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