Publications

Below is a list of my publications. Please see my Google Scholar profile for a full list of citations and co-authors, or my DBLP COMPUTER SCIENCE BIBLIOGRAPHY profile.

2017

Nicola Di Mauro, Antonio Vergari, Teresa M.A. Basile, Floriana Esposito. Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks, In: ECML/PKDD 2017: proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017 [ BibTeX | Abstract | PDF ]

Antonio Vergari, Robert Peharz, Nicola Di Mauro, Floriana Esposito. Encoding and Decoding Representations with Sum- and Max-Product Networks, In: ICLR 2017 Workshop track: Proceedings of the 5th International Conference on Learning Representations, 2017 [ BibTeX | Abstract | PDF ]

@InProceedings{dimauro16pgm,
 title = {Encoding and Decoding Representations with Sum- and Max-Product Networks},
 author = {Antonio Vergari, Robert Peharz, Nicola {Di Mauro}, Floriana Esposito},
 booktitle = {Proceedings of the 5th International Conference on Learning Representations},
 year = {2017}
}

Abstract. Sum-Product Networks (SPNs) are deep density estimators allowing exact and tractable inference. While up to now SPNs have been employed as black-box inference machines, we exploit them as feature extractors for unsupervised Rep- resentation Learning. Representations learned by SPNs are rich probabilistic and hierarchical part-based features. SPNs converted into Max-Product Networks (MPNs) provide a way to decode these representations back to the original input space. In extensive experiments, SPN and MPN encoding and decoding schemes prove highly competitive for Multi-Label Classification tasks.

2016

Antonio Vergari, Di Mauro Nicola and Floriana Esposito. Visualizing and Understanding Sum-Product Networks. CoRR, abs/1608.02341, 2016. [ BibTeX | PDF | arXiv ]

@article{vergari16corrb, author = {Antonio Vergari and Di Mauro, Nicola and Floriana Esposito}, title = { Visualizing and Understanding Sum-Product Networks }, journal = {CoRR}, volume = {abs/1608.08266}, year = {2016}, ee = {http://arxiv.org/abs/1608.08266} }

Antonio Vergari, Di Mauro Nicola and Floriana Esposito. Towards Representation Learning with Tractable Probabilistic Models. CoRR, abs/1608.02341, 2016. [ BibTeX | PDF | arXiv ]

@article{vergari16corra, author = {Antonio Vergari and Di Mauro, Nicola and Floriana Esposito}, title = {Towards Representation Learning with Tractable Probabilistic Models}, journal = {CoRR}, volume = {abs/1608.02341}, year = {2016}, ee = {http://arxiv.org/abs/1608.02341} }

Nicola Di Mauro, Antonio Vergari, and Floriana Esposito. Multi-Label Classification with Cutset Networks, In: A. Antonucci, G. Corani, C.P. de Campos (eds.) PGM 2016: Proceedings of the Eighth International Conference on Probabilistic Graphical Models, JMLR Workshop and Conference Proceedings, 2016 [ BibTeX | Abstract | PDF ]

@InProceedings{dimauro16pgm,
 title = {Multi-Label Classification with Cutset Networks},
 author = {Nicola {Di Mauro} and Antonio Vergari and Floriana Esposito},
 booktitle = {PGM 2016: Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
 editor = {A. Antonucci, G. Corani, C.P. de Campos},
 year = {2016},
 pages = {147-158},
 publisher = {JMLR Workshop and Conference Proceedings},
 volume = {52}
}

Abstract. In this work, we tackle the problem of Multi-Label Classification (MLC) by using Cutset Networks (CNets), weighted probabilistic model trees, recently proposed as tractable probabilistic models for discrete distributions. We employ CNets to perform Most Probable Explanation (MPE) inference exactly and efficiently and we improve a state-of-the-art structure learning algorithm for CNets by explicitly taking advantage of label dependencies. We achieve this by forcing the tree inner nodes to represent only feature variables and by exploiting structural heuristics while learning the leaf models. A thorough experimental evaluation on ten real-world datasets shows how the proposed approach improves several metrics for MLC, proving it to be competitive with problem transformation methods like classifier chains.

2015

Nicola Di Mauro, Antonio Vergari, and Teresa M.A. Basile. Learning Bayesian Random Cutset Forests, In F. Esposito et al. (eds.), ISMIS 2015, LNAI 9384, pp. 1-11, Springer, 2015. [ BibTeX | Abstract | PDF ]

	 @InProceedings{dimauro15ismis,
   Title                    = {Learning Bayesian Random Cutset Forests},
   Author                   = {Nicola {Di Mauro} and Antonio Vergari and Teresa M.A. Basile},
   Booktitle                = {ISMIS},
   editor 									= {F. Esposito et al.},
   Year                     = {2015},
	 pages 										= {1-11},
	 publisher 								= {Springer},
	 series    								= {LNAI},
	 volume    								= {9384}
	 }

Nicola Di Mauro, Antonio Vergari, and Floriana Esposito. Learning Accurate Cutset Networks by Exploiting Decomposability, In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds.) AI*IA 2015: Advances in Artificial Intelligence, LNAI 9336, 1-12, Springer, 2015 [ BibTeX | Abstract | PDF ]

	 @InProceedings{dimauro15aixia,
   Title                    = {Learning Accurate Cutset Networks by Exploiting Decomposability},
   Author                   = {Nicola {Di Mauro} and Antonio Vergari and Floriana Esposito},
   Booktitle                = {AI*IA 2015: Advances in Artificial Intelligence},
   editor 									= {Marco Gavanelli and Evelina Lamma and Fabrizio Riguzzi},
   Year                     = {2015},
	 pages 										= {1-12},
	 publisher 								= {Springer},
	 series    								= {LNCS},
	 volume    								= {9336}
	 }

Abstract. The rising interest around tractable Probabilistic Graphical Models is due to the guarantees on inference feasibility they provide. Among them, Cutset Networks (CNets) have recently been introduced as models embedding Pearl’s cutset conditioning algorithm in the form of weighted probabilistic model trees with tree-structured models as leaves. Learning the structure of CNets has been tackled as a greedy search leveraging heuristics from decision tree learning. Even if efficient, the learned models are far from being accurate in terms of likelihood. Here, we exploit the decomposable score of CNets to learn their structure and parameters by directly maximizing the likelihood, including the BIC criterion and informative priors on smoothing parameters. In addition, we show how to create mixtures of CNets by adopting a well known bagging method from the discriminative framework as an effective and cheap alternative to the classical EM. We compare our algorithms against the original variants on a set of standard benchmarks for graphical model structure learning, empirically proving our claims.

Antonio Vergari, Nicola Di Mauro, and Floriana Esposito. Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning. In ECML/PKDD, LNCS, 343-358, Springer, 2015. [ BibTeX | Abstract | PDF ]

	 @InProceedings{vergari15ecml.pdf,
   Title                    = {Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning},
   Author                   = {Antonio Vergari and Nicola {Di Mauro} and Floriana Esposito},
   Booktitle                = {ECML/PKDD (2) 2015},
   editor 									= {Marco Gavanelli and Evelina Lamma and Fabrizio Riguzzi},
   Year                     = {2015},
	 pages 										= {343-358},
	 publisher 								= {Springer},
	 series    								= {LNCS}
	 }

Abstract. The need for feasible inference in Probabilistic Graphical Models (PGMs) has lead to tractable models like Sum-Product Networks (SPNs). Their highly expressive power and their ability to provide exact and tractable inference make them very attractive for several real world applications, from computer vision to NLP. Recently, great attention around SPNs has focused on structure learning, leading to different algorithms being able to learn both the network and its parameters from data. Here, we enhance one of the best structure learner, , aiming to improve both the structural quality of the learned networks and their achieved likelihoods. Our algorithmic variations are able to learn simpler, deeper and more robust networks. These results have been obtained by exploiting some insights in the building process done by , by hybridizing the network adopting tree-structured models as leaves, and by blending bagging estimations into mixture creation. We prove our claims by empirically evaluating the learned SPNs on several benchmark datasets against other competitive SPN and PGM structure learners.

Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi. Bandit-Based Monte-Carlo Structure Learning of Probabilistic Logic Programs. In Machine Learning Journal, 100 (1), 2015. [ BibTeX | PDF ]

	 @article{dimauro15mlj.pdf,
	   author    = {Nicola Di Mauro and Elena Bellodi and Fabrizio Riguzzi},
		 title     = {Bandit-based Monte-Carlo structure learning of probabilistic logic programs},
		 journal   = {Machine Learning},
		 volume    = {100},
		 number    = {1},
		 pages     = {127--156},
		 year      = {2015}
	 }
2014

Teresa M.A. Basile, Nicola Di Mauro, and Floriana Esposito. Assessing Document Relevance by modeling Citation Networks with Probabilistic Graphs. Procedia Computer Science, 38:68–75, Elsevier, 2014. [ BibTeX | PDF ]

	 @article{basile14pcs,
	 journal={Procedia Computer Science},
	 doi={10.1016/j.procs.2014.10.013},
	 title={Assessing Document Relevance by modeling Citation Networks with Probabilistic Graphs},
	 publisher={Elsevier},
	 author={Basile, Teresa M.A. and Di Mauro, Nicola and Esposito, Floriana},
	 year={2014},
	 volume = {38},
	 pages = {68-75}
	 }

Nicola Di Mauro, Claudio Taranto, and Floriana Esposito. Link classification with probabilistic graphs. Journal of Intelligent Information Systems, 42:181–206, Springer US, 2014. [ BibTeX | Abstract | PDF ]

	 @article{ndm14jiis,
	 issn={0925-9902},
	 journal={Journal of Intelligent Information Systems},
	 doi={10.1007/s10844-013-0293-0},
	 title={Link classification with probabilistic graphs},
	 publisher={Springer US},
	 author={Di Mauro, Nicola and Taranto, Claudio and Esposito, Floriana},
	 year={2014},
	 volume = {42},
	 pages = {181-206}
	 }

Abstract. The need to deal with the inherent uncertainty in real-world relational or networked data leads to the proposal of new probabilistic models, such as probabilistic graphs. Every edge in a probabilistic graph is associated with a probability whose value represents the likelihood of its existence, or the strength of the relation between the entities it connects. The aim of this paper is to propose two machine learning techniques for the link classification problem in relational data exploiting the probabilistic graph representation. Both the proposed methods will exploit a language-constrained reachability method to infer the probability of possible hidden relationships that may exists between two nodes in a probabilistic graph. Each hidden relationships between two nodes may be viewed as a feature (or a factor), and its corresponding probability as its weight, while an observed relationship is considered as a positive instance for its corresponding link label. Given a training set of observed links, the first learning approach is to use a propositionalization technique adopting a L2-regularized Logistic Regression to learn a model able to predict unobserved link labels. Since in some cases the edges’ probability may be not known in advance or they could not be precisely defined for a classification task, the second proposed approach is to exploit the inference method and to use a mean squared technique to learn the edges’ probabilities. Both the proposed methods have been evaluated on real world data sets and the corresponding results proved their validity.

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Grasp and Path-Relinking for Coalition Structure Generation. Fundamenta Informaticae, 129(3):251–277, IOS Press, 2014. [ BibTeX | Abstract | PDF ]

	 @article{ndm14fi,
	 journal={Fundamenta Informaticae},
	 doi={10.3233/FI-2014-971},
	 title={Grasp and Path-Relinking for Coalition Structure Generation},
	 publisher={IOS Press},
	 author={Di Mauro, Nicola and Basile, Teresa M.A. and Ferilli, Stefano and Esposito, Floriana},
	 year={2014},
	 pages     = {251-277}
	 }

Abstract. In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition (maximizing a social welfare) of a set of entities involved in a system. The solution of the CSG problem finds applications in many fields such as Machine Learning (set covering machines, clustering), Data Mining (decision tree, discretization), Graph Theory, Natural Language Processing (aggregation), Semantic Web (service composition), and Bioinformatics. The problem of finding the optimal coalition structure is NP-complete. In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.

2013

Leonardo Capone, Nicola Di Mauro, and Floriana Esposito. Optimizing a static greedy algorithm for influence maximization. In CONGRESS0 NAZIONALE AICA 2013: Frontiere Digitali: dal Digital Divide alla Smart Society, 2013. [ BibTeX | PDF ]

	 @inproceedings{capone13aica,
	 author = {Leonardo Capone and Nicola {Di Mauro} and Floriana Esposito},
	 title = {Optimizing a static greedy algorithm for influence maximization},
	 booktitle = {CONGRESS0 NAZIONALE AICA 2013: Frontiere Digitali: dal Digital Divide alla Smart Society},
	 pages = {1093-1097},
	 year = {2013}
	 }

Nicola Di Mauro, Floriana Esposito, and Stefano Ferilli. Finding Critical Cells in Web Tables with SRL: Trying to Uncover the Devil’s Tease. In Proceedings of the Twelfth International Conference on Document Analysis and Recognition (ICDAR 2013), 2013. [ BibTeX | PDF ]

	 @inproceedings{ndm13icdar,
	 author = {Nicola {Di Mauro} and Floriana Esposito and Stefano Ferilli},
	 title = {Finding Critical Cells in Web Tables with SRL: Trying to Uncover the Devil's Tease},
	 booktitle = {Proceedings of the Twelfth International Conference on Document Analysis and Recognition  (ICDAR 2013)},
	 pages = {1093-1097},
	 year = {2013}
	 }

Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi. Bandit-Based Monte-Carlo Structure Learning of Probabilistic Logic Programs. In 23rd International Conference on Inductive Logic Programming, 2013. [ BibTeX | Abstract | PDF ]

	 @inproceedings{ndm13ilp,
	 author = {Nicola {Di Mauro} and Elena Bellodi and Fabrizio Riguzzi},
	 title = {Bandit-Based Monte-Carlo Structure Learning of Probabilistic Logic Programs},
	 booktitle = {23rd International Conference on Inductive Logic Programming}
	 year = {2013}
	 }

Probabilistic logic programming allows to model domains with complex and uncertain relationships among entities. While the problem of learning the parameters of such programs has been consid- ered by various authors, the problem of learning their structure is yet to be explored in depth. In this work we present an approximate search method based on a one-player game approach, called LEMUR. It relies on the Monte-Carlo tree search UCT algorithm that combines the precision of tree search with the generality of random sampling. LEMUR works by modifying the UCT algorithm in a similar fashion to FUSE, that considers a nite unknown horizon and deals with the problem of having a huge branching factor. The proposed system has been tested on the UW-CSE and Hepatitis datasets and has shown better performances than those of SLIPCASE and LSM.

Di Mauro, Nicola and Floriana Esposito. Ensemble Relational Learning based on Selective Propositionalization. CoRR, abs/1311.3735, 2013. [ BibTeX | PDF | arXiv ]

	 @article{ndm13corr,
	 author    = {Di Mauro, Nicola and Floriana Esposito},
	 title     = {Ensemble Relational Learning based on Selective Propositionalization},
	 journal   = {CoRR},
	 volume    = {abs/1311.3735},
	 year      = {2013},
	 ee        = {http://arxiv.org/abs/1311.3735}

}

Di Mauro, Nicola, Paolo Frasconi, Fabrizio Angiulli, Davide Bacciu, Marco de Gemmis, Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, Marco Gori, Francesca A. Lisi, Pasquale Lops, Donato Malerba, Alessio Micheli, Marcello Pelillo, Francesco Ricci, Fabrizio Riguzzi, Lorenza Saitta, and Giovanni Semeraro. Italian Machine Learning and Data Mining research: The last years. Intelligenza Artificiale, 7(2):77–89, 2013. [ BibTeX | PDF ]

	 @article{ndm13iaj,
	 author    = {Di Mauro, Nicola and
           Paolo Frasconi and
           Fabrizio Angiulli and
           Davide Bacciu and
           Marco de Gemmis and
           Floriana Esposito and
           Nicola Fanizzi and
           Stefano Ferilli and
           Marco Gori and
           Francesca A. Lisi and
           Pasquale Lops and
           Donato Malerba and
           Alessio Micheli and
           Marcello Pelillo and
           Francesco Ricci and
           Fabrizio Riguzzi and
           Lorenza Saitta and
           Giovanni Semeraro},
	 title     = {Italian Machine Learning and Data Mining research: The last years},
	 journal   = {Intelligenza Artificiale},
	 volume    = {7},
	 number    = {2},
	 year      = {2013},
	 pages     = {77-89},

}

Claudio Taranto, Nicola Di Mauro, and Floriana Esposito. Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns. In New Frontiers in Mining Complex Patterns - First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012 (Revised Selected Papers), pp. 155–169, Springer, 2013. [ BibTeX | PDF ]

	 @inproceedings{taranto13mcp,
	 author    = {Claudio Taranto and Nicola Di Mauro and  Floriana Esposito},
	 title     = {Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns},
	 editor    = {Annalisa Appice and
           Michelangelo Ceci and
           Corrado Loglisci and
           Giuseppe Manco and
           Elio Masciari and
           Zbigniew W. Ras},
	 booktitle     = {New Frontiers in Mining Complex Patterns - First
	 International Workshop, NFMCP 2012, Held in Conjunction with
	 ECML/PKDD 2012 (Revised Selected Papers)},
	 year      = {2013},
	 pages     = {155-169},
	 publisher = {Springer},
	 series    = {LNCS},
	 ee        = {http://dx.doi.org/10.1007/978-3-642-37382-4_11},
	 isbn      = {978-3-642-37381-7},
	 volume    = {7765}
	 }
2012

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Social networks and statistical relational learning: a survey. International Journal of Social Network Mining, 1(2):185–208, 2012. [ BibTeX | PDF ]

	 @article{esposito12ijsnm,
	 author = {Floriana Esposito and Stefano Ferilli and Teresa M.A. Basile and Nicola {Di~Mauro}},
	 title = {Social networks and statistical relational learning: a survey},
	 journal = {International Journal of Social Network Mining},
	 volume = {1},
	 year = {2012},
	 number = {2},
	 pages = {185--208},
	 }

Nicola Di Mauro, Stefano Ferilli and Floriana Esposito. Learning to Recognize Critical Cells in Document Tables. In 8th Italian Research Conference on Digital Libraries (IRCDL), pp. 105–116, Springer-Verlag, 2012. [ BibTeX | PDF ]

	 @inproceedings{dimauro12ircdl,
	 author    = {Nicola Di Mauro, Stefano Ferilli and Floriana Esposito},
	 title     = {Learning to Recognize Critical Cells in Document Tables},
	 booktitle = {8th Italian Research Conference on Digital Libraries (IRCDL)},
	 year      = {2012},
	 pages     = {105-116},
	 editor    = {M. Agosti et al.},
	 publisher = {Springer-Verlag},
	 series    = {CCIS},
	 volume    = {354},
	 }

Claudio Taranto, Nicola Di Mauro and Floriana Esposito. Learning in Probabilistic Graphs exploiting Language-Constrained Patterns. In New Frontiers in Mining Complex Patters, ECML-PKDD12 Workshop, 2012. [ BibTeX | PDF ]

	 @inproceedings{taranto12nfmcp,
	 author    = {Claudio Taranto, Nicola Di Mauro and Floriana Esposito},
	 title     = {Learning in Probabilistic Graphs exploiting Language-Constrained Patterns},
	 booktitle = {New Frontiers in Mining Complex Patters, ECML-PKDD12 Workshop},
	 year      = {2012},
	 }

Claudio Taranto, Nicola Di Mauro and Floriana Esposito. Uncertain Graphs meet Collaborative Filtering. In 3rd Italian Information Retrieval Workshop, 2012. [ BibTeX | PDF ]

	 @inproceedings{taranto12iir,
	 author    = {Claudio Taranto, Nicola Di Mauro and Floriana Esposito},
	 title     = {Uncertain Graphs meet Collaborative Filtering},
	 booktitle = {3rd Italian Information Retrieval Workshop},
	 year      = {2012},
	 }

Claudio Taranto, Nicola Di Mauro and Floriana Esposito . Uncertain (Multi)graphs for Personalization Services in Digital Libraries. In 8th Italian Research Conference on Digital Libraries, pp. 141–152, Springer-Verlag, 2012. [ BibTeX | PDF ]

	 @inproceedings{taranto12ircdl,
	 author    = {Claudio Taranto, Nicola Di Mauro and Floriana Esposito },
	 title     = {Uncertain (Multi)graphs for Personalization Services in Digital Libraries},
	 booktitle = {8th Italian Research Conference on Digital Libraries},
	 year      = {2012},
	 pages     = {141-152},
	 editor    = {M. Agosti et al.},
	 publisher = {Springer-Verlag},
	 series    = {CCIS},
	 volume    = {354},
	 }

Fabrizio Riguzzi and Nicola Di Mauro. Applying the Information Bottleneck to Statistical Relational Learning. Machine Learning Journal, 86(1):89–114, 2012. (see the accompanying technical report “Application of the Information Bottleneck to LPAD Learning) [ BibTeX | PDF ]

	 @article{riguzzi12mlj,
	 	author = {Fabrizio Riguzzi and Nicola {Di~Mauro}},
		title = {Applying the Information Bottleneck to Statistical Relational Learning},
		journal = {Machine Learning Journal},
		volume = {86},
		year = {2012},
		number = {1},
		pages = {89--114},
		note = {(<u>see the accompanying technical report "Application of the Information Bottleneck to	{LPAD} Learning</u>)},
	}
2011

Floriana Esposito, Nicola Di Mauro, Claudio Taranto, and Stefano Ferilli. Computational Models Enhancing Semantic Access to Digital Repositories. In 7th Italian Research Conference on Digital Libraries, pp. 107–110, Springer, 2011. [ BibTeX ]

	 @inproceedings{esposito11ircdl,
	 author    = {Floriana Esposito and Nicola Di Mauro and Claudio Taranto and Stefano Ferilli},
	 title     = {Computational Models Enhancing Semantic Access to Digital Repositories},
	 booktitle = {7th Italian Research Conference on Digital Libraries},
	 year      = {2011},
	 pages     = {107-110},
	 editor    = {Maristella Agosti and
           Floriana Esposito and
           Carlo Meghini and
           Nicola Orio},
	 publisher = {Springer},
	 series    = {CCIS},
	 volume    = {249}
	 }

Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, Teresa Maria Altomare Basile, and Marenglen Biba. DDTA - Digitalisation of Districts in the Textile and Clothing Sector. In 7th Italian Research Conference on Digital Libraries, pp. 119–122, Springer, 2011. [ BibTeX ]

	 @inproceedings{esposito11ircdl-b,
	 author    = {Floriana Esposito and
           Stefano Ferilli and
           Nicola Di Mauro and
           Teresa Maria Altomare Basile and
           Marenglen Biba},
	 title     = {DDTA - Digitalisation of Districts in the Textile and Clothing
           Sector},
	 booktitle = {7th Italian Research Conference on Digital Libraries},
	 year      = {2011},
	 pages     = {119-122},
	 editor    = {Maristella Agosti and
           Floriana Esposito and
           Carlo Meghini and
           Nicola Orio},
	 publisher = {Springer},
	 series    = {CCIS},
	 volume    = {249}
	 }

Floriana Esposito, Teresa M.A. Basile, Nicola Di Mauro, and Stefano Ferilli. A Relational Approach to Sensor Network Data Mining. In A. Soro, E. Vargiu, G. Armano, and G. Paddeu, editors, Information Retrieval and Mining in Distributed Environments, Studies in Computational Intelligence, pp. 163–181, Springer, 2011. [ BibTeX | PDF ]

	 @incollection{esposito11irmde,
	 author = {Floriana Esposito and Teresa M.A. Basile and Nicola {Di Mauro} and Stefano Ferilli},
	 title = {A Relational Approach to Sensor Network Data Mining},
	 booktitle = {Information Retrieval and Mining in Distributed Environments},
	 publisher = {Springer},
	 series = {Studies in Computational Intelligence},
	 editor = {A. Soro and E. Vargiu and G. Armano and G. Paddeu},
	 year = {2011},
	 pages = {163--181},
	 volume = {324}
	 }

Stefano Ferilli, Teresa M.A. Basile, Nicola Di Mauro, and Floriana Esposito. Plugging Numeric Similarity in First-Order Logic Horn Clauses Comparison. In XIIth International Conference of the Italian Association for Artificial Intelligence, pp. 33–44, Springer, 2011. [ BibTeX ]

	 @inproceedings{ferilli11aiia,
	 author    = {Stefano Ferilli and Teresa M.A. Basile and Nicola Di Mauro and Floriana Esposito},
	 title     = {Plugging Numeric Similarity in First-Order Logic Horn Clauses Comparison},
	 booktitle = {XIIth International Conference of the Italian Association for Artificial Intelligence},
	 year      = {2011},
	 pages     = {33-44},
	 editor    = {Roberto Pirrone and Filippo Sorbello},
	 publisher = {Springer},
	 series    = {LNCS},
	 volume    = {6934}
	 }

Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Floriana Esposito. A Taxonomic Generalization Technique for Natural Language Processing. In 19th International Symposium on Methodologies for Intelligent Systems, pp. 418–427, Springer, 2011. [ BibTeX | PDF ]

	 @inproceedings{ferilli11ismis,
	 author    = {Stefano Ferilli and Nicola {Di Mauro} and Teresa M.A. Basile and Floriana Esposito},
	 title     = {A Taxonomic Generalization Technique for Natural Language Processing},
	 year      = {2011},
	 pages     = {418--427},
	 booktitle = {19th International Symposium on Methodologies for Intelligent Systems},
	 editor    = {Marzena Kryszkiewicz and Henryk Rybinski and Andrzej Skowron and Zbigniew W. Ras},
	 publisher = {Springer},
	 series    = {LNCS}
	 }

Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Markov Logic Networks for Document Layout Correction. In The Twenty-fourth International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 275–284, Springer, 2011. [ BibTeX | PDF ]

	 @inproceedings{ferilli11ieaaie,
	 author    = {Stefano Ferilli and Teresa M.A. Basile and Nicola {Di Mauro}},
	 title     = {Markov Logic Networks for Document Layout Correction},
	 year      = {2011},
	 pages     = {275--284},
	 booktitle = {The Twenty-fourth International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
	 editor = {Kishan G. Mehrotra and Chilukuri K. Mohan and Jae C. Oh and Pramod K. Varshney and Moonis Ali},
	 publisher = {Springer},
	 series    = {LNCS}
	 }

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. mLynx: Relational Mutual Information. In 21st International Conference on Inductive Logic Programming: Latest Advances in Inductive Logic Programming, Imperial College Press, 2011. [ BibTeX | PDF ]

	 @inproceedings{ndm11ilp,
	 author    = {Nicola {Di Mauro} and Teresa M.A. Basile and Stefano Ferilli and Floriana Esposito},
	 title     = {mLynx: Relational Mutual Information},
	 publisher = {Imperial College Press},
	 year      = {2011},
	 booktitle = {21st International Conference on Inductive Logic Programming: Latest Advances in Inductive Logic Programming},
	 }

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. GRASP and path-relinking for Coalition Structure Generation. Technical Report, arXiv:1103.1157, pp. 1–24, 2011. [ BibTeX | PDF ]

	 @article{ndm11arxiv,
	 author =  {Nicola {Di Mauro} and Teresa M.A. Basile and Stefano Ferilli and  Floriana Esposito},
	 title =   {GRASP and path-relinking for Coalition Structure Generation},
	 year =    {2011},
	 journal = {Technical Report, arXiv:1103.1157},
	 pages =   {1-24}
	 }

Nicola Di Mauro and Donato Malerba. Mining Networked Data. In Symposium on Computational Intelligence and Data Mining (IEEE-CIDM11), pp. xx, IEEE, 2011. [ BibTeX | PDF ]

	 @inproceedings{ndm11cidm,
	 author    = {Nicola {Di Mauro} and Donato Malerba},
	 title     = {Mining Networked Data},
	 year      = {2011},
	 pages     = {xx},
	 editor    = {Nitesh Chawla and Irwin King and Alessandro Sperduti},
	 booktitle = {Symposium on Computational Intelligence and Data Mining (IEEE-CIDM11)},
	 publisher = {IEEE}
	 }

Claudio Taranto, Nicola Di Mauro, and Floriana Esposito. rsLDA: a Bayesian Hierarchical Model for Relational Learning. In International Conference on Data and Knowledge Engineering, IEEE, 2011. [ BibTeX | PDF ]

	 @inproceedings{taranto11icdke,
	 author    = {Claudio Taranto and Nicola {Di Mauro} and Floriana Esposito},
	 title     = {rsLDA: a Bayesian Hierarchical Model for Relational Learning},
	 year      = {2011},
	 booktitle = {International Conference on Data and Knowledge Engineering},
	 publisher = {IEEE}
	 }

Claudio Taranto, Nicola Di Mauro, and Floriana Esposito. Probabilistic Inference over Image Networks. In 7th Italian Research Conference on Digital Libraries, 2011. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Optimizing Probabilistic Models for Relational Sequence Learning. In 19th International Symposium on Methodologies for Intelligent Systems, pp. 240–249, Springer, 2011. [ BibTeX | PDF ]

2010

Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. Classifying Agent Behaviour through Relational Sequential Patterns. In Agent and Multi-Agent Systems: Technologies and Applications (KES-AMSTA10), pp. 273–282, Springer, 2010. [ BibTeX | PDF ]

Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. Relational Sequence based Classification in Multi-agent Systems. In Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART10), pp. 619–622, INSTICC Press, 2010. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, and Stefano Ferilli, and Floriana Esposito. Coalition Structure Generation with GRASP. In The 14th International Conference on Artificial Intelligence: Methodology, Systems, Applications, Springer, 2010. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. GRASP for the Coalition Structure Formation Problem. Technical Report, arXiv:1004.2880, pp. 1–12, 2010. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Feature Construction for Relational Sequence Learning. Technical Report, arXiv:1006.5188, pp. 1–15, 2010. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, and Stefano Ferilli, and Floriana Esposito. Approximate relational reasoning by stochastic propositionalization. In Zbigniew W. Ras and Li-Shiang Tsay, editors, Advances in Information and Intelligent Systems, Studies in Computational Intelligence, pp. 81–109, Springer, 2010. [ BibTeX | PDF ]

Fabrizio Riguzzi and Nicola Di Mauro. Applying the Information Bottleneck Approach to SRL: Learning LPAD Parameters. In The 20th International Conference on Inductive Logic Programming (ILP10), 2010. (see the accompanying technical report “Application of the Information Bottleneck to LPAD Learning) [ BibTeX | PDF ]

Fabrizio Riguzzi and Nicola Di Mauro. Application of the Information Bottleneck to LPAD Learning. Technical Report CS-2010-01, Dipartimento di Ingegneria, Università di Ferrara, Italy, 2010. (Technical report accompanying the papers “Applying the Information Bottleneck Approach to SRL: Learning LPAD Parameters” and “Applying the Information Bottleneck to Statistical Relational Learning”) [ BibTeX | PDF ]

Claudio Taranto, Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. Approximate image color correlograms. In Proceedings of the 18th International Conference on Multimedia, pp. 1127–1130, ACM, 2010. [ BibTeX | PDF ]

2009

Teresa M.A. Basile, Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. Relational Temporal Data Mining for Wireless Sensor Networks. In Emergent Perspectives in Artificial Intelligence, XIth International Conference of the Italian Association for Artificial Intelligence (AI*IA09), pp. 416–425, Springer, 2009. [ BibTeX | PDF ]

Grazia Bombini, Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. A Logic Programming Framework for Learning by Imitation. In Proceedings of the 11th International Conference on Enterprise Information Systems, pp. 218–223, 2009. [ BibTeX | PDF ]

Grazia Bombini, Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Relational Learning by Imitation. In Agent and Multi-Agent Systems: Technologies and Applications (KES-AMSTA09), pp. 273–282, Springer, 2009. [ BibTeX | PDF ]

Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. Incremental learning from positive examples. In 24-esimo Convegno Italiano di Logica Computazionale, 2009. [ BibTeX | PDF ]

Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. Relational Sequence Clustering for Aggregating Similar Agents. In Foundations of Intelligent Systems (ISMIS09), pp. 361–370, Springer, 2009. [ BibTeX | PDF ]

Floriana Esposito, Teresa M.A. Basile, Nicola Di Mauro, and Stefano Ferilli. Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems. In P. Grifoni, editors, Multimodal Human Computer Interaction and Pervasive Services, pp. 121–138, Information Science Reference, 2009. [ BibTeX ]

Stefano Ferilli, Marenglen Biba, Nicola Di Mauro, Teresa M.A. Basile, and Floriana Esposito. Plugging Taxonomic Similarity in First-Order Logic Horn Clauses Comparison. In Emergent Perspectives in Artificial Intelligence, XIth International Conference of the Italian Association for Artificial Intelligence (AI*IA09), pp. 131–140, Springer, 2009. [ BibTeX | PDF ]

Stefano Ferilli, Floriana Esposito, Marenglen Biba, Teresa M.A. Basile, and Nicola Di Mauro. FOL Learning for Knowledge Discovery in Documents. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp. 348–374, Information Science Reference, 2009. [ BibTeX | PDF ]

Stefano Ferilli, Teresa M.A. Basile, Marenglen Biba, Nicola Di Mauro, and Floriana Esposito. A General Similarity Framework for Horn Clause Logic. Fundamenta Informaticae, 90(1-2):43–66, IOS Press, 2009. [ BibTeX | PDF ]

2008

Floriana Esposito, Nicola Di Mauro, Teresa M.A. Basile, and Stefano Ferilli. Multi-Dimensional Relational Sequence Mining. Fundamenta Informaticae, 89(1):23–43, IOS Press, 2008. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Machine Learning for Digital Document Processing: From Layout Analysis To Metadata Extraction. In S. Marinai and H. Fujisawa, editors, Machine Learning in Document Analysis and Recognition, Studies in Computational Intelligence, pp. 105–138, Springer, 2008. [ BibTeX | PDF ]

Stefano Ferilli, Marenglen Biba, Teresa M.A. Basile, Nicola Di Mauro, and Floriana Esposito. k-Nearest Neighbor Classification on First-Order Logic Descriptions. In Workshop on Reliability Issues in Knowledge Discovery (RIKD 2008), Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 202–210, IEEE Computer Society, 2008. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Grazia Bombini, Stefano Ferilli, and Floriana Esposito. Eliciting Multi-Dimensional Relational Patterns. In 23-esimo Convegno Italiano di Logica Computazionale, 2008. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Stochastic Propositionalization for Efficient Multi-relational Learning. In Foundations of Intelligent Systems, 17th International Symposium, ISMIS 2008, Toronto, Canada, May 20-23, 2008, Proceedings, pp. 78–83, Springer, 2008. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Approximate reasoning for efficient anytime induction from relational knowledge bases. In Proceedings of the Second International Conference on Scalable Uncertainty Management, pp. 160–173, Springer, 2008. [ BibTeX | PDF ]

2007

Marenglen Biba, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks. In Proceedings of the 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2007), pp. 132–139, Springer, 2007. [ BibTeX | PDF ]

Marenglen Biba, Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Multi-class Protein Fold Recognition Through a Symbolic-Statistical Framework. In Proceedings of the 7th International Workshop on Fuzzy Logic and Applications (WILF 2007) - Special Session Fourth International Meeting on Computational Intelligence Methods for Bioinformatics Biostatistics (CIBB 2007), pp. 666–673, Springer, 2007. [ BibTeX | PDF ]

Marenglen Biba, Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. Mining Time-series Sequences of Reactions for Biologically Active Patterns in Metabolic Pathways. In Proceedings of the Fifteenth Italian Symposium on Advanced Database Systems (SEBD 2007), pp. 40–51, 2007. [ BibTeX | PDF ]

Marenglen Biba, Stefano Ferilli, Floriana Esposito, Nicola Di Mauro, and Teresa M.A. Basile. A Fast Partial Memory Approach to Incremental Learning through an Advanced Data Storage Framework. In Proceedings of the Fifteenth Italian Symposium on Advanced Database Systems (SEBD 2007), pp. 52–63, 2007. [ BibTeX | PDF ]

Marenglen Biba, Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. Unsupervised Discretization Using Kernel Density Estimation. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI07), pp. 696–701, 2007. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Inference of Abduction Theories for Handling Incompleteness in First-Order Learning. Knowledge and Information Systems (KAIS) journal, Special Issue on Mining Low Quality Data, 11(2):217–242, 2007. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. Incremental Learning of First Order Logic Theories for the Automatic Annotations of Web Documents. In Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2, pp. 1093–1097, IEEE Computer Society, Washington, DC, USA, 2007. [ BibTeX | PDF ]

Stefano Ferilli, Teresa M.A. Basile, Nicola Di Mauro, Marenglen Biba, and Floriana Esposito. Similarity-Guided Clause Generalization. In Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on Artificial Intelligence and Human-Oriented Computing (AI*IA 2007), pp. 278–289, Springer, 2007. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, Marenglen Biba, and Floriana Esposito. Generalization-based Similarity for Conceptual Clustering. In Proceedings of the Third International Workshop on Mining Complex Data (MCD07), pp. 13–24, 2007. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, and Floriana Esposito. Mining Frequent Patterns from Multi-Dimensional Relational Sequences. In Proceedings of the 6th Workshop on Multi-Relational Data Mining (MRDM07), pp. 22–33, 2007. [ BibTeX | PDF ]

2006

Marenglen Biba, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. Intelligent Methodologies for Scientific Conference Management. In Foundations of Intelligent Systems (ISMIS06), pp. 258–267, Springer, 2006. [ BibTeX | PDF ]

Marenglen Biba, Stefano Ferilli, Teresa M.A Basile, Nicola Di Mauro, and Floriana Esposito. Induction of Abstraction Theories Using Unsupervised Discretization of Continuous Attributes. In 16th Int. Conf. on Inductive Logic Programming - Short Papers, pp. 22–24, 2006. [ BibTeX | PDF ]

Marco Degemmis, Pasquale Lops, Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Giovanni Semeraro. Text learning for user profiling in e-commerce. International Journal of Systems Science, 37(13):905–918, Taylor & Francis, 2006. [ BibTeX | PDF ]

Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Multistrategy Operators for Relational Learning and Their Cooperation. Fundamenta Informaticae, 69(4):389–409, IOS Press, 2006. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Automatic Content-based Indexing of Digital Documents through Intelligent Processing Techniques. In Proceedings of the Second International Workshop on Document Image Analysis for Libraries (DIAL06), pp. 204–219, IEEE Computer Society, 2006. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. Towards Automatic Digital Library Content-based Management. In Post-proceedings of the 2nd Italian Research Conference on Digital Library Management Systems (IRCDL 2006), pp. 49–50, DELOS: a Network of Excellence on Digital Libraries, 2006. [ BibTeX | PDF ]

Nicola Fanizzi, Luigi Iannone, Nicola Di Mauro, and Floriana Esposito. Tractable Feature Generation Through Description Logics with Value and Number Restrictions. In Proceedings of The 19th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE’06), pp. 629–638, Springer, 2006. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, Floriana Esposito, and Marenglen Biba. Automatic Topics Identification For Reviewer Assignment. In Proceedings of The 19th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE’06), pp. 721–730, Springer, 2006. [ BibTeX | PDF ]

Stefano Ferilli, Teresa M.A Basile, Nicola Di Mauro, Marenglen Biba, and Floriana Esposito. A New Similarity Measure for Guiding Generalizations Search. In 16th Int. Conf. on Inductive Logic Programming - Short Papers, pp. 71–73, 2006. [ BibTeX | PDF ]

Stefano Ferilli, Floriana Esposito, Nicola Di Mauro, Teresa M.A. Basile, and Marenglen Biba. An Abduction framework for Handling Incompleteness in First-Order Learning. In Proceedings of the ECAI-2006 Workshop on Abduction and Induction in AI and Scientific Modelling (AIAI-2006), pp. 24–27, 2006. [ BibTeX | PDF ]

Nicola Di Mauro, Floriana Esposito, Teresa M.A Basile, and Stefano Ferilli. Random Searching the ILP Lattice. In 16th Int. Conf. on Inductive Logic Programming - Short Papers, pp. 55–57, 2006. [ BibTeX | PDF ]

2005

Teresa M. A. Basile, Floriana Esposito, Nicola Di Mauro, and Stefano Ferilli. Handling continuous-valued attributes in Incremental First-Order Rules Learning. In Advances in Artificial Intelligence (AI*IA05), pp. 430–441, Springer, 2005. [ BibTeX | PDF ]

Marco Degemmis, Pasquale Lops, Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Giovanni Semeraro. Learning User Profiles from Text in e-Commerce. In The First International Conference on Advanced Data Mining and Applications (ADMA05), pp. 370–381, Springer, 2005. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Document Image Understanding for Digital Libraries Access. In Post-proceedings of the First Italian Research Conference on Digital Library Management Systems (IRCDL05), pp. 32–41, Padova, 2005. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Semantic-Based Access to Digital Document Databases. In Foundations of Intelligent Systems (ISMIS05), pp. 373–381, Springer, 2005. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M. A. Basile, and Nicola Di Mauro. Intelligent Document Processing. In International Conference on Document Analysis and Recognition (ICDAR05), IEEE Computer Society, 2005. August 29, September 1 - Seoul, Korea. [ BibTeX | PDF ]

Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro, and Floriana Esposito. On the LearnAbility of Abstraction Theories from Observations for Relational Learning. In Machine Learning: ECML05, pp. 120–132, Springer, 2005. [ BibTeX | PDF ]

Stefano Ferilli, Teresa M.A. Basile, Nicola Di Mauro, and Floriana Esposito. Automatic Induction of Abduction and Abstraction Theories from Observations. In Inductive Logic Programming, pp. 103–120, Springer, 2005. [ BibTeX | PDF ]

Nicola Di Mauro. First-Order Incremental Theory Revision. Ph.D. Thesis, Department of Computer Science, University of Bari, 2005. [ BibTeX ]

Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, and Teresa M.A. Basile. Avoiding Order Effects in Incremental Learning. In Advances in Artificial Intelligence (AI*IA05), pp. 110–121, Springer, 2005. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, and Stefano Ferilli. GRAPE: An Expert Review Assignment Component for Scientific Conference Management Systems. In Innovations in Applied Artificial Intelligence (IEA/AIE05), pp. 789–798, Springer, 2005. [ BibTeX | PDF ]

2004

Teresa M.A. Basile, Stefano Ferilli, Nicola Di Mauro, and Floriana Esposito. Incremental Induction of Classification Rules for Cultural Heritage Documents. In Innovations in Applied Artificial Intelligence (IEA/AIE04), pp. 915–934, Springer, 2004. [ BibTeX | PDF ]

Marco Degemmis, Pasquale Lops, Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Giovanni Semeraro. A Relevance Feedback Method for Discovering User Profiles from Text. In ECML/PKDD04 - Proceedings of the Workshop W1 on Statistical Approaches to Web Mining (SAWM04), pp. 111–122, 2004. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Nicola Fanizzi, Teresa M.A. Basile, and Nicola Di Mauro. Incremental Learning and Concept Drift in INTHELEX. Intelligent Data Analysis Journal, Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, 8(3):213–237, IOS Press, 2004. [ BibTeX | PDF ]

Floriana Esposito, Giovanni Semeraro, Stefano Ferilli, Marco Degemmis, Nicola Di Mauro, Teresa M.A. Basile, and Pasquale Lops. Evaluation and Validation of Two Approaches to User Profiling. In Web Mining: From Web to Semantic Web, First European Web Mining Forum (EMWF03), pp. 130–147, Springer, 2004. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, and Nicola Di Mauro. Discoverig Logical Structures in Digital Documents. In Intelligent Information Processing and Web Mining (IIPWM04), pp. 513–521, Springer, 2004. [ BibTeX | PDF ]

Floriana Esposito, Donato Malerba, Giovanni Semeraro, Stefano Ferilli, Oronzo Altamura, Teresa M.A. Basile, Margherita Berardi, Michelangelo Ceci, and Nicola Di Mauro. Machine Learning methods for automatically processing historical documents: from paper acquisition to XML transformation. In Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL04), pp. 328–335, IEEE Computer Society, 2004. [ BibTeX | PDF ]

Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, and Nicola Di Mauro. Automatic Induction of First-Order Logic Descriptors Type Domains from Observations. In Inductive Logic Programming, pp. 116–131, Springer, 2004. [ BibTeX | PDF ]

Stefano Ferilli, Luigi Iannone, Giovanni Semeraro, Teresa M.A. Basile, Nicola Di Mauro, and Ignazio Palmisano. Annotazione automatica di Documenti Storici Cartacei. Intelligenza Artificiale, rivista dell’associazione italiana per l’intelligenza artificiale, I(1):44–49, 2004. [ BibTeX ]

Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, and Nicola Di Mauro. Induzione Automatica di Regole per la Classificazione e l’Interpretazione di Documenti Storici del Patrimonio Culturale. In Atti del XLII Congresso Annuale AICA - Ricerca ed Impresa: Conoscenza e Produzione per la Società dell’Informazione, pp. 621–633, 2004. [ BibTeX ]

Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, and Nicola Di Mauro. Automatic Induction of Domain-related Information: Learning Descriptors Type Domains. In Proceedings 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 1011–1012, IOS Press, 2004. [ BibTeX | PDF ]

Oriana Licchelli, Teresa M.A. Basile, Nicola Di Mauro, Floriana Esposito, Giovanni Semeraro, and Stefano Ferilli. Machine Learning Approaches for Inducing Student Models. In Innovations in Applied Artificial Intelligence (IEA/AIE04), pp. 935–944, Springer, 2004. [ BibTeX | PDF ]

Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, and Teresa M.A. Basile. An Algorithm for Incremental Mode Induction. In Innovations in Applied Artificial Intelligence (IEA/AIE04), pp. 512–522, Springer, 2004. [ BibTeX | PDF ]

Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, and Teresa M.A. Basile. A Backtracking Strategy for Order-Independent Incremental Learning. In Proceedings 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 460–464, IOS Press, 2004. [ BibTeX | PDF ]

Giovanni Semeraro, Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, Nicola Di Mauro, Luigi Iannone, and Ignazio Palmisano. Automatic Management of Annotations on Cultural Heritage Material. In Proceedings of the International Conference on Digital Libraries (ICDL04), pp. 805–812, The Energy and Resources Institute (TERI), 2004. [ BibTeX | PDF ]

2003

Teresa M.A. Basile, Marco Degemmis, Nicola Di Mauro, Floriana Esposito, and Stefano Ferilli. Symbolic and Probabilistic Techniques for Learning User Profiles. In Proceedings of the 10th ISPE International Conference on Concurrent Engineering: Research and Applications (CE-2003), pp. 451–456, A.A. Balkema Publishers, 2003. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Nicola Fanizzi, Teresa M.A. Basile, and Nicola Di Mauro. Incremental Multistrategy Learning for Document Processing. Applied Artificial Intelligence: An Internationa Journal, 17(8/9):859–883, Taylor & Francis, 2003. [ BibTeX | PDF ]

Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, Luigi Iannone, Ignazio Palmisano, and Giovanni Semeraro. Improving Automatic Labelling through RDF Management. In Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access (ICADL03), pp. 578–589, Springer, 2003. [ BibTeX | PDF ]

Floriana Esposito, Giovanni Semeraro, Stefano Ferilli, Marco Degemmis, Nicola Di Mauro, Teresa M.A. Basile, and Pasquale Lops. Evaluation and Validation of Two Approaches to User Profiling. In Web Mining: From Web to Semantic Web, First European Web Mining Forum (EMWF03), pp. 51–63, 2003. [ BibTeX | PDF ]

Nicola Fanizzi, Stefano Ferilli, Nicola Di Mauro, and Teresa M.A. Basile. Spaces of Theories with Ideal Refinement Operators. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI03), pp. 527–532, Morgan Kaufmann Publishers, 2003. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Floriana Esposito. θ-subsumption and Resolution: A New Algorithm. In Foundations of Intelligent Systems (ISMIS03), pp. 384–391, Springer, 2003. [ BibTeX | PDF ]

Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, and Nicola Di Mauro. Automatic Induction of Rules for Classification and Interpretation of Cultural Heritage Material. In Research and Advanced Technology for Digital Libraries: 7th European Conference (ECDL03), pp. 152–163, Springer, 2003. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Floriana Esposito. A complete Subsumption Algorithm. In AI*IA 2003: Advances in Artificial Intelligence, pp. 1–13, Springer, 2003. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Floriana Esposito. Incremental Induction of Rules for Document Image Understanding. In AI*IA 2003: Advances in Artificial Intelligence, pp. 176–188, Springer, 2003. [ BibTeX | PDF ]

Stefano Ferilli, Luigi Iannone, Ignazio Palmisano, Giovanni Semeraro, Teresa M.A. Basile, and Nicola Di Mauro. Automatic Annotation of Historical Paper Documents. In Proceedings of the AI*IA 2003 Workshop on Artificial Intelligence for the Cultural Heritage, pp. 99–103, 2003. [ BibTeX | PDF ]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, Floriana Esposito, and Nicola Fanizzi. An Exhaustive Matching Procedure for the Improvement of Learning Efficiency. In Inductive Logic Programming: 13th International Conference (ILP03), pp. 112–129, Springer, 2003. [ BibTeX | PDF ]

2002

Floriana Esposito, Stefano Ferilli, Nicola Fanizzi, Teresa M.A. Basile, and Nicola Di Mauro. Cooperation of Multiple Strategies for Automated Learning in Complex Environments. In Foundations of Intelligent Systems, 13th International Symposium (ISMIS02), pp. 574–582, Springer, 2002. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Fanizzi, Nicola Di Mauro, and Teresa M.A. Basile. Efficient Theta-subsumption under Object Identity. In Atti del Workshop AI*IA 2002 su Apprendimento Automatico: Metodi e Applicazioni dell’Ottavo Convegno della Associazione Italiana per l’Intelligenza Artificiale, pp. 59–68, 2002. [ BibTeX | PDF ]

Stefano Ferilli, Nicola Fanizzi, Teresa M.A. Basile, and Nicola Di Mauro. Learning Family Relationships Exploiting Multistrategy. In Atti del Workshop AI*IA 2002 su Apprendimento Automatico: Metodi e Applicazioni dell’Ottavo Convegno della Associazione Italiana per l’Intelligenza Artificiale, pp. 71–80, 2002. [ BibTeX | PDF ]

Giovanni Semeraro, Floriana Esposito, Stefano Ferilli, Nicola Fanizzi, Teresa M.A. Basile, and Nicola Di Mauro. Multistrategy Learning of Rules for Automated Classification of Cultural Heritage Material. In Digital Libraries: People, Knowledge, and Technology, 5th International Conference on Asian Digital Libraries (ICADL02), pp. 182–193, Springer, 2002. [ BibTeX | PDF ]

2001

Teresa M.A. Basile, Bruno Belsanti, Nicola Di Mauro, Antonio Di Palma, and Stefano Ferilli. INTHELEX: INcremental THEory Learner from Examples. In Atti della Sessione DEMO del Settimo Congresso della Associazione Italiana per l’Intelligenza Artificiale, AI*IA01, pp. 52–55, 2001. [ BibTeX | PDF ]