2004.bib

@article{LamMelRig04-CJ-IJ,
  author = {Evelina  Lamma and Paola Mello and Fabrizio Riguzzi},
  title = {A System for Measuring Function Points from an {ER}-{DFD} Specification},
  journal = {The Computer Journal},
  abstract = {We present a tool for measuring the Function Point
software metric from the specification of a software system
expressed in the form of an Entity Relationship diagram plus a
Data Flow Diagram (ER-DFD).  First, the informal and general
Function Point counting rules are translated into rigorous rules
expressing properties of the ER-DFD.  Then, the rigorous rules
are translated into Prolog.  The measures given by the system on
a number of case studies are in accordance with those of human
experts.},
  publisher = {Oxford University Press},
  address = {Oxford, \UK},
  keywords = {Software Engineering, Software Metrics, Function Points},
  year = {2004},
  volume = {47},
  number = {3},
  pages = {358--372},
  month = may,
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/fun.pdf},
  http = {http://comjnl.oxfordjournals.org/cgi/reprint/47/3/358},
  issn = {0010--4620},
  doi = {10.1093/comjnl/47.3.358},
  copyright = {Evelina  Lamma, Paola Mello and Fabrizio Riguzzi, licensed exclusively to The British Computer Society}
}
@inproceedings{LamRigSto04-IPMU04-IC,
  author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari},
  title = {Improving the K2 Algorithm Using Association Rules Parameters},
  booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems
    ({IPMU}2004), Perugia, 4-9 July 2004},
  editor = { B. Bouchon-Meunier and G. Coletti and R. R. Yager},
  abstract = {A Bayesian network is an appropriate tool to work with a sort of uncertainty
    and probability, that are typical of real-life applications. Bayesian network arcs represent
    statistical dependence between different variables.  In the data mining field, association
    rules can be interpreted as well as expressing statistical dependence relations.
    K2 is a well-known algorithm which is able to learn   Bayesian network.  In this paper we
    want to  present an extension of K2 called K2-rules that exploits a parameter normally
     defined in relation to association rules for learning  Bayesian networks.
     The experiments performed show that K2-rules improves K2 with respect to both
      the quality of the learned network and the execution
    time. },
  year = {2004},
  month = jul,
  publisher = {Editrice Universit\`{a} La Sapienza},
  address = {\Rome, \Italy},
  pages = {1667--1674},
  url = {http://ipmu2004.dipmat.unipg.it/},
  keywords = {Bayesian Networks Learning},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamRigSto-IPMU04.pdf}
}
@inproceedings{LamRigSto04-ECAI04-IC,
  author = {Evelina Lamma and Fabrizio Riguzzi and Sergio Storari},
  title = {Exploiting Association and Correlation Rules Parameters for Improving the K2 Algorithm},
  booktitle = {Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004, including Prestigious Applicants
               of Intelligent Systems, PAIS 2004, Valencia, Spain, August
               22-27, 2004},
  editor = {Ramon Lopez de Mantaras and Lorenza Saitta},
  abstract = {A Bayesian network is an appropriate tool to deal with the uncertainty that
    is typical of real-life applications. Bayesian network arcs represent statistical dependence
    between different variables.  In the data mining field, association and correlation rules
    can be interpreted as well as expressing statistical dependence relations.
    K2 is a well-known algorithm which is able to learn Bayesian network.
    In this paper we present two extensions of K2 called K2-Lift and K2-X2 that
    exploit two parameters normally defined in relation to association and correlation rules for
    learning Bayesian networks.  The experiments performed show that K2-Lift and K2-X2 improve K2
    with respect to both the quality of the learned network and the execution time.},
  year = {2004},
  month = aug,
  publisher = {{IOS} Press},
  address = {Amsterdam, \TheNetherlands},
  pages = {500-504},
  keywords = {Bayesian Networks Learning},
  isbn = {1-58603-452-9},
  isbn = {9781586034528},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/LamRigSto-ECAI04.pdf},
  url = {http://www.frontiersinai.com/ecai/ecai2004/ecai04/pdf/p0500.pdf},
  series = {Frontiers in Artificial Intelligence and Applications},
  issn = {0922-6389},
  volume = {110}
}
@inproceedings{Rig04-ILP04-IC,
  author = {Fabrizio Riguzzi},
  title = {Learning Logic Programs with Annotated Disjunctions},
  booktitle = {Inductive Logic Programming: 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004. Proceedings},
  editor = {A Srinivasan and R. King},
  abstract = {Logic Programs with Annotated Disjunctions (LPADs) provide a
simple and elegant framework for integrating probabilistic
reasoning and logic programming. In this paper we propose an
algorithm for learning LPADs. The learning problem we consider
consists in starting from a sets of interpretations annotated
with their probability and finding one (or more) LPAD that assign
to each interpretation the associated probability. The learning
algorithm first finds all the disjunctive clauses that are true
in all interpretations, then it assigns to each disjunct in the
head a probability and finally decides how to combine the clauses
to form an LPAD by solving a constraint satisfaction problem. We
show that the learning algorithm is correct and complete.},
  year = {2004},
  month = sep,
  publisher = {Springer Verlag},
  address = {Heidelberg, \Germany},
  keywords = {Logic Programming, Inductive Logic Programming, Probabilistic Logic
    Programming},
  series = {Lecture Notes in Artificial Intelligence},
  volume = {3194},
  pages = {270--287},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-ILP04.pdf},
  http = {http://link.springer.com/chapter/10.1007%2F978-3-540-30109-7_21},
  isbn = {3-540-22941-8},
  issn = {0302-9743},
  doi = {10.1007/978-3-540-30109-7_21},
  copyright = {Springer},
  wos = {WOS:000223999400021},
  scopus = {2s2.022944492698}
}
@inproceedings{Rig04-DISCCHALL-IW,
  author = {Fabrizio Riguzzi},
  title = {Classification and visualization on the hepatitis dataset},
  booktitle = {ECML/PKDD 2004 Discovery Challenge, Pisa, 20-24 September 2004},
  editor = {Petr Berka and Bruno Cremilleux},
  abstract = {In this paper we address  goals 2 and 3 of those proposed by the
donors of the Hepatitis dataset, namely to evaluate  whether it
is possible to estimate the stage of liver fibrosis from the
results of examinations, and to evaluate the effectiveness of the
interferon therapy. Goal 2 was addressed by learning various
classifiers that predict the value of fibrosis from the values
of  examinations other than the biopsy. Unfortunately, the best
accuracy obtained was only 50.6 \%, up only 2.1 \% from the
performance of the default classifier, thus showing that
replacing biopsies is still very hard if not impossible. As
regards goal 3, we have plotted the distribution of the values of
the difference in fibrosis and in activity before and after the
interferon therapy. The plots show that the therapy actually
reduces the level of activity but not the level of fibrosis.
Moreover, we have also plotted the distribution of the values of
the difference of GOT before and after the therapy. The graph
shows that a moderate reduction of GOT is obtained.},
  year = {2004},
  month = sep,
  keywords = {Classification, Visualization},
  url = {http://lisp.vse.cz/challenge/ecmlpkdd2004/final/riguzzi.ps},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-DISCCHALL04.pdf}
}
@article{RigSto04-NJ,
  author = {Fabrizio Riguzzi and Sergio Storari},
  title = {La Sedicesima Conferenza Europea di Intelligenza
    Artificiale ({ECAI})},
  journal = {Intelligenza Artificiale},
  abstract = {Relazione sulla Sedicesima Conferenza Europea di Intelligenza
    Artificiale ({ECAI})},
  year = {2004},
  volume = {Anno {I}},
  number = {4},
  month = dec,
  issn = {1724-8035},
  pages = {57--60},
  publisher = {Associazione Italiana per l'Intelligenza
  Artificiale {AI*IA}},
  address = {Bari, \Italy},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigSto-IA04.pdf}
}

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