Baldoni-1997.bib

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@comment{{Command line: bib2bib --remove owner --remove authordip --remove typepublication --remove nationality -s $date -r -c 'year = 1997' -c 'author : "Baldoni" or editor : "Baldoni"' -ob Baldoni-1997.bib -oc Baldoni-1997.txt bibliography.bib}}

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@inproceedings{BGMP97-TIPO_LN,
  author = {Baldoni, M. and Giordano, L. and Martelli, A. and Patti, V.},
  title = {{A}n {A}bductive {P}rocedure for {R}easoning about {A}ctions in {M}odal
	{L}ogic {P}rogramming},
  booktitle = {Proc. of the 2nd International Workshop on Non-Monotonic Extentions
	of Logic Programming, NMELP'96},
  year = {1997},
  editor = {Dix, J. and Pereira, L. M. and Przymusinski, T. C.},
  volume = {1216},
  series = {LNAI},
  pages = {132-150},
  publisher = {Springer-Verlag},
  issn = {0302-9743},
  isbn = {3-540-42672-8},
  doi = {10.1007/BFb0023805},
  keywords = {logic_programming, reasoning, actions},
  numberpages = {19},
  pdf = {http://www.di.unito.it/~argo/papers/1997_WNMELP.pdf},
  postscript = {http://www.di.unito.it/\~{}argo/papers/1997_WNMELP.ps.gz},
  url = {http://www.springer.de/cgi/svcat/search_book.pl?isbn=3-540-62843-6}
}

@inproceedings{BBCL97-TIPO_LN,
  author = {Baldoni, M. and Baroglio, C. and Cavagnino, D. and {Lo~Bello}, G.},
  title = {{E}xtraction of {D}iscriminant {F}eatures from {I}mage {F}ractal
	{E}ncoding},
  booktitle = {Proc. of AI*IA 97: Advances in Artificial Intelligence},
  year = {1997},
  editor = {Lenzerini, M.},
  volume = {1321},
  series = {LNAI},
  pages = {127-138},
  publisher = {Springer-Verlag},
  abstract = {In this paper we face the problem of finding characteristic information
	about images of different objects, showing that the fractal encoding
	based on Iterated Function Systems, besides allowing very high compression
	rates, can be successfully applied also for capturing discriminatory
	features that can be exploited for non-fractal image classification.
	An original feature extraction algorithm was developed and applied
	to encode the hand-written digits data set. Then, different learning
	algorithms were applied and their performances were compared both
	to those obtained using a general purpose fractal encoder (enc by
	Fisher) and to the work done in the StatLog project on the same data
	set.},
  isbn = {3-540-63576-9},
  issn = {0302-9743},
  doi = {10.1007/3-540-63576-9_102},
  keywords = {IFS, image_recognition},
  numberpages = {12},
  pdf = {http://www.di.unito.it/~baldoni/fractals/papers/ai_ia97.pdf},
  postscript = {http://www.di.unito.it/\~{}baldoni/fractals/papers/ai_ia97.ps.gz}
}

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