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docs/index/iclp07.tex
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@ -1122,29 +1122,27 @@ difference in this benchmark.
\subsection{Performance of \JITI on ILP applications} \label{sec:perf:ILP} \subsection{Performance of \JITI on ILP applications} \label{sec:perf:ILP}
%------------------------------------------------------------------------- %-------------------------------------------------------------------------
The need for \JITI was originally noticed in inductive logic The need for \JITI was originally noticed in inductive logic
programming applications. programming applications. Table~\ref{tab:ilp:time} shows JITI
Table~\ref{tab:ilp:time} shows JITI performance on some learning tasks performance on some learning tasks using the ALEPH
using the ALEPH system~\cite{ALEPH}. The dataset \Krki tries to system~\cite{ALEPH}. The dataset \Krki tries to learn rules from a
learn rules from a small database of chess end-games; small database of chess end-games; \GeneExpression learns rules for
\GeneExpression learns rules for yeast gene activity given a yeast gene activity given a database of genes, their interactions, and
database of genes, their interactions, and micro-array gene expression micro-array gene expression data; \BreastCancer processes real-life
data; \BreastCancer processes real-life patient reports towards patient reports towards predicting whether an abnormality may be
predicting whether an abnormality may be malignant; malignant; \IEProtein processes information extraction from paper
\IEProtein processes information extraction from abstracts to search proteins; \Susi learns from shopping patterns; and
paper abstracts to search proteins; \Susi learns from shopping \Mesh learns rules for finite-methods mesh design. The datasets
patterns; and \Mesh learns rules for finite-methods mesh \Carcinogenesis, \Choline, \Mutagenesis, \Pyrimidines, and
design. The datasets \Carcinogenesis, \Choline, \Thermolysin try to predict chemical properties of compounds. The
\Mutagenesis, \Pyrimidines, and \Thermolysin try to first three datasets store properties of interest as tables, but
predict chemical properties of compounds. The first three
datasets store properties of interest as tables, but
\Thermolysin learns from the 3D-structure of a molecule's \Thermolysin learns from the 3D-structure of a molecule's
conformations. Several of these datasets are standard across the Machine conformations. Several of these datasets are standard across the
Learning literature. \GeneExpression~\cite{} and Machine Learning literature. \GeneExpression~\cite{ilp-regulatory06}
\BreastCancer~\cite{} were partly developed by some of the and \BreastCancer~\cite{DBLP:conf/ijcai/DavisBDPRCS05} were partly
paper's authors. Most datasets perform simple queries in an developed by some of the paper's authors. Most datasets perform simple
extensional database. The exception is \Mutagenesis where queries in an extensional database. The exception is \Mutagenesis
several predicates are defined intensionally, requiring extensive where several predicates are defined intensionally, requiring
computation. extensive computation.
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
\begin{table}[t] \begin{table}[t]

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docs/index/lp.bib
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@ -567,3 +567,43 @@ institution = "Department of Artificial Intelligence, University of Edinburgh"
isbn = {1-58113-634-X}, isbn = {1-58113-634-X},
bibsource = {DBLP, http://dblp.uni-trier.de} bibsource = {DBLP, http://dblp.uni-trier.de}
} }
@manual{ ALEPH,
author = "Ashwin Srinivasan",
title = "The Aleph Manual",
url = "http://oldwww.comlab.ox.ac.uk/oucl/groups/machlearn/Aleph/aleph_toc.html.",
year = "2001" }
@inproceedings{ilp-regulatory06,
author = {Irene M. Ong and
Scott E. Topper and
David Page and
Santos Costa, V\{\i}tor},
title = {Inferring Regulatory Networks from Time Series Expression Data and
Relational Data via Inductive Logic Programming},
booktitle = {Proceedings of the Sixteenth International Conference on Inductive Logic Programming, Santiago de Compostela, Spain},
year = {2007}
}
@inproceedings{DBLP:conf/ijcai/DavisBDPRCS05,
author = {Jesse Davis and
Elizabeth S. Burnside and
In{\^e}s Dutra and
David Page and
Raghu Ramakrishnan and
Santos Costa, V\'{\i}tor and
Jude W. Shavlik},
title = {View Learning for Statistical Relational Learning: With
an Application to Mammography.},
pages = {677-683},
editor = {Leslie Pack Kaelbling and
Alessandro Saffiotti},
booktitle = {IJCAI-05, Proceedings of the Nineteenth International Joint
Conference on Artificial Intelligence, Edinburgh, Scotland,
UK, July 30-August 5, 2005},
publisher = {Professional Book Center},
isbn = {0938075934},
year = {2005},
bibsource = {DBLP, http://dblp.uni-trier.de}
}