2005.bib

@inproceedings{Rig05-RCRA05-NW,
  author = {Fabrizio Riguzzi},
  title = {A Comparison of {ILP} Systems on the {Sisyphus} Dataset},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/rcra2005cr%20riguzzi.pdf},
  booktitle = {Incontro del Gruppo di Lavoro
    Rappresentazione della Conoscenza e Ragionamento Automatico
    ({RCRA}) dell'Associazione Italiana per l'Intelligenza Artificiale ({AI*IA}) dal titolo
    ``Analisi Sperimentale e Benchmark di Algoritmi per l'Intelligenza Artificiale'',  10 giugno 2005},
  keywords = {Machine Learning, Inductive Logic Programming},
  abstract = {In this paper we present a comparison of two Inductive Logic
Programming (ILP) systems on the Sisyphus dataset. The aim of the
comparison is to to show how the systems behave on a large
dataset. The considered  systems are Aleph and Tilde. Both
systems have an unacceptable execution time on the whole dataset,
so they are run over  samples extracted from the dataset.
The comparison shows that, on average, Tilde finds more accurate
theories in a smaller time.
},
  editor = {Marco Cadoli and Marco Gavanelli and Tony Mancini},
  month = jun,
  year = {2005},
  address = {Ferrara, \Italy},
  issn = {1724-8035}
}
@inproceedings{GamLamRig05-IDA05-IC,
  author = {Giacomo Gamberoni and Evelina Lamma and Fabrizio Riguzzi and  Sergio Storari and Stefano Volinia},
  title = {Bayesian Networks Learning for Gene Expression Datasets},
  booktitle = {Advances in Intelligent Data Analysis VI: 6th International Symposium on Intelligent Data Analysis, {IDA} 2005, Madrid, Spain, \September\  8-10, 2005. Proceedings},
  year = {2005},
  publisher = {Springer Verlag},
  address = {Heidelberg, \Germany},
  month = sep,
  series = {Lecture Notes in Computer Science},
  volume = {3646},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  pages = {109--120},
  isbn = {3-540-28795-7},
  issn = {0302-9743},
  doi = {10.1007/11552253_11},
  http = {http://dx.medra.org/10.1007/11552253_11},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/GamLamRig-IDA05.pdf},
  copyright = {Springer}
}
@inproceedings{Rig05-ILP05LateBreakingPapers-IW,
  author = {Fabrizio Riguzzi},
  title = {Two Results Regarding Refinement Operators},
  booktitle = {Late Breaking Papers, 15th International Workshop on Inductive Logic Programming
    ({ILP}05), Bonn, Germany, \August\  10--13, 2005},
  year = 2005,
  editor = {S. Kramer and B. Pfahringer},
  month = jul,
  publisher = {Technische Universit\"{a}t M\"{u}nchen},
  note = {Report {TUM}--{I0510}},
  address = {M\"{u}nchen, \Germany},
  pages = {53--58},
  abstract = {In this paper we present two results regarding refinement
operators. The first is that it does not exist a refinement
operator that is both complete and optimal for the
theta-subsumption ordering and for the language of full
cla\USAl logic.
The second regards the properties of the refinement operator
implemented in Aleph's code by predicate auto\_refine/2.
We think this operator is interesting for its simplicity and
because it does not require the construction of a bottom-clause.
In particular, the operator is useful in the cases where a
bottom-clause can not be built, as for example in learning from
interpretations. The properties of this operator are that it  is
locally finite, not proper nor complete but weakly complete.
Moreover, the operator is also not optimal. However, it can be
made complete by extending the specification of the language bias
and by requiring that the language does not contain function
symbols.},
  keywords = {Inductive Logic Programming, Refinement Operators},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-ILP05.pdf}
}
@inproceedings{Rig05-ILP05DiscChall-IW,
  author = {Fabrizio Riguzzi},
  title = {A Simple Approach to a Multi-Label Classification Problem},
  booktitle = {Discovery Challenge, Late Breaking Papers, 15th International Workshop on Inductive Logic Programming
    ({ILP}05), Bonn, Germany,  \August\  10--13, 2005},
  year = 2005,
  editor = {S. Kramer and B. Pfahringer},
  month = jul,
  publisher = {Technische Universit\"{a}t M\"{u}nchen},
  note = {Report {TUM}--{I0510}},
  address = {M\"{u}nchen, \Germany},
  pages = {105--110},
  abstract = {The approach to handle multiple label for each gene is to have a
learning problem for each label that appears in \texttt{yeast.labelled}.
In each learning problem, a gene is a positive example if it
contains that label, otherwise it is a negative example.
In this way we learn one classifier for each label. To label
unseen genes, we run each generated classifier on the gene data
and we assign the label to the gene if the classifiers gives a
positive answer.
As a classifier, we have used Tilde for its speed and
good accuracy. In order to finish the experiments before the deadline
we had to consider only a subset of the available data, namely the
protein secondary structure data.},
  keywords = {Inductive Logic Programming,  Multiple Label Classification, Gene Ontology},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-ILP05chall.pdf}
}
@article{Rig05-IA-NJ,
  author = {Fabrizio Riguzzi},
  title = {A Comparison of {ILP} Systems on the {Sisyphus} Dataset},
  journal = {Intelligenza Artificiale},
  number = {3},
  volume = {Anno II},
  month = sep,
  year = {2005},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-IA05.pdf},
  pdf = {http://ia.di.uniba.it/magazine/2005_3/indice.html},
  pages = {52--59},
  abstract = {In this paper we present a comparison between two
Inductive Logic Programming (ILP) systems on the Sisyphus dataset.
The aim of this comparison is to investigate the behaviour of two
state of the art ILP systems on a ``large'' dataset. The
comparison shows that the limitations of ILP systems regards
mainly the execution time rather then the memory requirements.},
  keywords = {Machine Learning, Inductive Logic Programming,
Relational Databases},
  publisher = {Associazione Italiana per l'Intelligenza
  Artificiale {AI*IA}},
  address = {Bari, \Italy},
  issn = {1724-8035},
  keywords = {Inductive Logic Programming, Machine Learning}
}

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