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1
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0037311077
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FS-FOIL: An Inductive learning method for extracting interpretable fuzzy descriptions
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Bodenhofer, U., Drobics, M., Klement, E-P.: FS-FOIL: An Inductive learning method for extracting interpretable fuzzy descriptions. Int.J. Approximate Reasoning 32, 131-152 (2003)
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(2003)
Int.J. Approximate Reasoning
, vol.32
, pp. 131-152
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Bodenhofer, U.1
Drobics, M.2
Klement, E.-P.3
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2
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22644449312
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Scaling up inductive logic programming by learning from interpretations
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Blockeel, H., Raedt, L.D., Jacobs, N.: Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3(1), 59-93 (1999)
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(1999)
Data Mining and Knowledge Discovery
, vol.3
, Issue.1
, pp. 59-93
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Blockeel, H.1
Raedt, L.D.2
Jacobs, N.3
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3
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38049137935
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Two Novel Methods for Learning Logic Prorams with Annotated Disjunctions
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Spain, UDC Press Service, pp, ISBN: 84-9749-206-4
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Blockeel, H: Two Novel Methods for Learning Logic Prorams with Annotated Disjunctions. In: Proceedings of short papers on ILP '06, Santiago De Compostela, Spain, UDC Press Service, pp. 31-33 (2006) ISBN: 84-9749-206-4
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Proceedings of short papers on ILP '06, Santiago De Compostela
, pp. 31-33
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Blockeel, H.1
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4
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0347316762
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Learning qualitative models
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Bratko, I., Šue, D.: Learning qualitative models. AI Magazine 24, 107-119 (2003)
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(2003)
AI Magazine
, vol.24
, pp. 107-119
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Bratko, I.1
Šue, D.2
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5
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22944487297
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De Raedt, L., Kersting, K.: Probabilistic Inductive Logic Programming. In: Ben-David, S., Case, J., Maruoka, A. (eds.) ALT 2004. LNCS (LNAI), 3244, pp. 19-36. Springer, Heidelberg (2004)
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De Raedt, L., Kersting, K.: Probabilistic Inductive Logic Programming. In: Ben-David, S., Case, J., Maruoka, A. (eds.) ALT 2004. LNCS (LNAI), vol. 3244, pp. 19-36. Springer, Heidelberg (2004)
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6
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9144267095
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An introduction to inductive logic programming
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Džeroski, S, Lavrač, N, eds, Springer, Heidelberg
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Džeroski, S., Lavrač, N.: An introduction to inductive logic programming. In: Džeroski, S., Lavrač, N. (eds.) Relational data mining, pp. 48-73. Springer, Heidelberg (2001)
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Relational data mining
, pp. 48-73
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Džeroski, S.1
Lavrač, N.2
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7
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0041779094
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Learning probabilistic relational models
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Džeroski, S, Lavrač, N, eds, Springer, Heidelberg
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Getoor, L., et al.: Learning probabilistic relational models. In: Džeroski, S., Lavrač, N. (eds.) Relational data mining, pp. 307-335. Springer, Heidelberg (2001)
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(2001)
Relational data mining
, pp. 307-335
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Getoor, L.1
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8
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0003846041
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A Tutorial on Learning with Bayesian Networks
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Technical Report MSR-TR-95-06, Microsoft Research March
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Heckerman, D.: A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research (March 1995)
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Heckerman, D.1
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9
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38049137934
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Unsupervised Learning of User Preferences by Ordinal Classification
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Bratislava, Slovakia, pp, ISBN 80-227-2468-8
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Horváth, T.: Unsupervised Learning of User Preferences by Ordinal Classification. In: Workshop NAZOU, Bystra Dolina, Slovakia, 2006: Vydavatelstvo STU. Bratislava, Slovakia, pp. 125-134 (2006) ISBN 80-227-2468-8
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(2006)
Workshop NAZOU, Bystra Dolina, Slovakia, 2006: Vydavatelstvo STU
, pp. 125-134
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Horváth, T.1
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10
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38049155186
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Measuring impact of information systems on business competitiveness using regression and ILP
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Košice, Slovakia, ISBN 80-225-1879-4
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Horváth, T., Sudzina, F.: Measuring impact of information systems on business competitiveness using regression and ILP. In: Ekonomika firiem, Michalovce, Slovakia, 2004: PHF EU Bratislava, Košice, Slovakia, 2004, pp. 296-300 (2004) ISBN 80-225-1879-4
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(2004)
Ekonomika firiem, Michalovce, Slovakia, 2004: PHF EU Bratislava
, pp. 296-300
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Horváth, T.1
Sudzina, F.2
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11
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38049140275
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GAP - Rule Discovery for Graded Classifciation
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Fuernkranz, J, eds, Pisa, Italy, TU Darmstadt, Darmstadt, Germany, 2004
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Horváth, T., Vojtáš, P.: GAP - Rule Discovery for Graded Classifciation. In: Fuernkranz, J. (eds.) Workshop of Advances in Inductive Rule Learning (W8) of ECML/PKDD '04, Pisa, Italy, 2004: TU Darmstadt, Darmstadt, Germany, 2004, pp. 46-63 (2004)
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(2004)
Workshop of Advances in Inductive Rule Learning (W8) of ECML/PKDD '04
, pp. 46-63
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Horváth, T.1
Vojtáš, P.2
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12
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38049186343
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Horváth, T., Vojtáš, P.: Fuzzy induction via generalized annotated programs. In: FDD '04, Dortmund, Germany, 2004, pp. 419-433. Springer, Heidelberg (2005)
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Horváth, T., Vojtáš, P.: Fuzzy induction via generalized annotated programs. In: FDD '04, Dortmund, Germany, 2004, pp. 419-433. Springer, Heidelberg (2005)
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13
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9444234690
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Bayesian Logic Programs
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Technical Report 151, University of Freiburg, 52 pages
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Kersting, K., De Raedt, L.: Bayesian Logic Programs, Technical Report 151, University of Freiburg, 52 pages
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Kersting, K.1
De Raedt, L.2
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14
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44049120122
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Theory of generalized annotated logic programming and its applications
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Kifer, M., Subrahmanian, V.S.: Theory of generalized annotated logic programming and its applications. J. Logic Programming 12, 335-367 (1992)
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(1992)
J. Logic Programming
, vol.12
, pp. 335-367
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Kifer, M.1
Subrahmanian, V.S.2
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15
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1842842037
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A comparison of fuzzy and annotated logic programming
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Krajči, S., Lencses, R., Vojtáš, P.: A comparison of fuzzy and annotated logic programming. Fuzzy Sets and Systems 144, 173-192 (2004)
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Fuzzy Sets and Systems
, vol.144
, pp. 173-192
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Krajči, S.1
Lencses, R.2
Vojtáš, P.3
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17
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9444227683
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Prade, H., Richard, G., Serrurier, M.: Enriching relational Learning with fuzzy predicates. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), 2838, pp. 399-410. Springer, Heidelberg (2003)
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Prade, H., Richard, G., Serrurier, M.: Enriching relational Learning with fuzzy predicates. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 399-410. Springer, Heidelberg (2003)
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18
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38049097268
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Learning first order fuzzy rules with their implication operator
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Prade, H., Richard, G., Dubois, D., Sudkamp, T., Serrurier, M.: Learning first order fuzzy rules with their implication operator. In. Proc. of IPMU (2004)
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Richard, G.2
Dubois, D.3
Sudkamp, T.4
Serrurier, M.5
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38049143346
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Project NAZOU
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Project NAZOU: http://nazou.fiit.stuba.sk/
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20
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22644449663
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An induction algorithm based on fuzzy logic programming
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Zhong, N, Zhou, L, eds, Methodologies for Knowledge Discovery and Data Mining, Springer, Heidelberg
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Shibata, D., et al.: An induction algorithm based on fuzzy logic programming. In: Zhong, N., Zhou, L. (eds.) Methodologies for Knowledge Discovery and Data Mining. LNCS (LNAI), vol. 1574, pp. 268-273. Springer, Heidelberg (1999)
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Shibata, D.1
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0035900850
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Fuzzy logic programming
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Vojtáš, P.: Fuzzy logic programming. Fuzzy Sets and Systems 124(3), 361-370 (2004)
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Fuzzy Sets and Systems
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38049099888
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Transformation of deductive and inductive tasks between models of logic programming with imperfect information
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Editrice Universita La Sapienza, Roma, pp
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Vojtáš, P., Vomlelová, M.: Transformation of deductive and inductive tasks between models of logic programming with imperfect information. In: Proceedings of IPMU, Editrice Universita La Sapienza, Roma, pp. 839-846 (2004)
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Vomlelová, M.2
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38049186342
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On models of comparison of multiple monotone classifications
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Paris, pp
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Vojtáš, P., Vomlelová, M.: On models of comparison of multiple monotone classifications. In: Proc. IPMU'2006, Paris, France, Éditions EDK, Paris, pp. 1236-1243
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Proc. IPMU'2006, Paris, France, Éditions EDK
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Vojtáš, P.1
Vomlelová, M.2
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