Pubblicazioni

Some relevant publications (a more complete list is avalable at DBLP and Google Scholar):

Papers in International Journals

• A. Appice, M. Ceci, A. Turi, & D. Malerba (2011). A Parallel, Distributed Algorithm for Relational Frequent Pattern Discovery from Very Large Data Sets, Intelligent Data Analysis, 15,1, 69–88.

• T. Weninger, F. Fumarola, R. Barber, J. Han, & D. Malerba (2010). Unexpected Results in Automatic List Extraction on the Web, SIGKDD Explorations, 12,2, 26-30.

• A. Turi, C. Loglisci , E. Salvemini, G. Grillo, D. Malerba & D. D'Elia (2009). Computational annotation of UTR cis-regulatory modules through Frequent Pattern Mining, BMC Bioinformatics, BioMed Central, 10, Suppl 6, S25.

• D. Malerba, M. Ceci & A. Appice (2009). A relational approach to probabilistic classification in a transductive setting, Engineering Applications of Artificial Intelligence, Elsevier, 22, 1, 109-116.

• D. Malerba (2008). A relational perspective on spatial data mining, Int. J. Data Mining, Modelling and Management,, Inderscience Enterprises, 1, 1, 103-118.

• M. Ceci & D. Malerba (2007). Classifying web documents in a hierarchy of categories: a comprehensive study, Journal of Intelligent Information Systems, Kluwer Academic Publishers, 28, 1, 37-78.

• O.Altamura, M. Berardi, M. Ceci, D. Malerba , & A. Varlaro (2007). Using colour information to understand censorship cards of film archives,International Journal of Document Analysis and Recognition, Springer Verlag, 9, 2, 281-297.

• M. Ceci, M. Berardi & D. Malerba (2007). Relational data mining and ILP for document image processing, Applied Artificial Intelligence, 21, 8, pp. 317-342.

• A. Appice, C. d'Amato, F. Esposito, & D. Malerba (2006). Classification of symbolic objects: A lazy learning approach, Intelligent Data Analysis, 10, 4, 301-324. (Software)

• D. Malerba, F. Esposito, M. Ceci, & A. Appice (2004). Top-Down Induction of Model Trees with Regression and Splitting Nodes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 5, 612-625. (Software & Data)

• F.A. Lisi, D. Malerba (2004). Inducing Multi-Level Association Rules from Multiple Relation, Machine Learning Journal, 55, 175-210. (Software & Data)

• D. Malerba (2003). Learning Recursive Theories in the Normal ILP Setting, Fundamenta Informaticae, 57, 1, 39-77. (Software & Data)

• A. Appice, M. Ceci, A. Lanza, F.A. Lisi, & D. Malerba (2003). Discovery of spatial association rules in geo-referenced census data: A relational mining approach, Intelligent Data Analysis, 7, 6, 541-566. (Software & Data)

• D. Malerba, F. Esposito, A. Lanza, F.A. Lisi & A. Appice (2003). Empowering a GIS with inductive learning capabilities: the case of INGENS, Journal of Computers, Environment, and Urban Systems, 27, 265-281.

• D. Malerba, F. Esposito, F.A. Lisi & A. Appice (2002). Mining spatial association rules in census data, Research in Official Statistics, 5, 1, 19-44.

• F. Esposito, D. Malerba & V. Marengo (2001). Inductive Learning from Numerical and Symbolic Data: An Integrated Framework, Intelligent Data Analysis, 5, 6, 445-461.

• O.Altamura, F. Esposito & D. Malerba (2001). Transforming Paper Documents into XML Format with WISDOM++, International Journal of Document Analysis and Recognition, Springer Verlag, 4, 1, 2-17.

• F. Esposito & D. Malerba (2001). Guest Editorial: Machine Learning in Computer Vision, Applied Artificial Intelligence, 15, 8, pp. 1-13.

• F. Esposito, D. Malerba & F.A. Lisi (2000). Machine Learning for Intelligent Processing of Printed Documents, Journal of Intelligent Information Systems, Kluwer Academic Publishers, 14, 2/3, 175-198.

• F. Esposito, D. Malerba, G. Semeraro & V. Tamma (1999). The Effects of Pruning Methods on the Predictive Accuracy of Induced Decision Trees, Applied Stochactic Models in Business and Industry (formerly Applied Stochastic Models in Data Analysis), 15, 4, pp. 277-299. (Potato routines used for experiments)

• F. Esposito, D. Malerba, G. Semeraro, N. Fanizzi, & S. Ferilli (1998). Adding machine learning and knowledge intensive techniques to a digital library service, International Journal on Digital Libraries, Kluwer Academic Publishers, 2, 1, 3-19.

• F. Esposito, A. Lanza, D. Malerba, & G. Semeraro (1997). Machine Learning for Map Interpretation: An Intelligent Tool for Environmental Planning.Applied Artificial Intelligence: An International Journal, 11, 10, 673-696.

• F. Esposito, D. Malerba, V. Ripa, and G. Semeraro (1997). Discovering Causal Rules in Relational Databases. Applied Artificial Intelligence: An International Journal, 11, 1, 71-83.

• F. Esposito, D. Malerba, & G. Semeraro (1997). A Comparative Analysis of Methods for Pruning Decision Trees, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-19, 5, 476-491. (Potato routines used for experiments)

• F. Esposito, D. Malerba, and G. Semeraro (1994). Multistrategy Learning for Document Recognition, Applied Artificial Intelligence: An International Journal, 8, 1, 33-84.

• F. Esposito, D. Malerba, & G. Semeraro (1992). Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-14, 3, 390-402.

   

  Chapters in International Books

• M. Ceci, C. Loglisci, E. Salvemini, D D’Elia & D. Malerba (2010). Mining Spatial Association Rules for Composite Motif Discovery . Chapter 5 in R. Bruni (Ed.), Mathematical Approaches to Polymer Sequence Analysis and Related Problems, , pp. 87-109, Springer.

• M. Ceci, A. Appice & D. Malerba (2010). Transductive Learning for Spatial Data Classification. In J. Koronacki et al. (Eds.): Advances in Machine Learning I, pp. 189-207, Springer.

• M. May, B. Berendt, A. Cornuejols, J. Gama, F. Giannotti, A. Hotho, D. Malerba, E. Menesalvas, K. Morik, R. Pedersen, L. Saitta, Y. Saygin, A. Schuster & K. Vanhoof (2009). Research Challenges in Ubiquitous Knowledge Discovery. Chapter 7 in H. Kargupta, J. Han, P.S. Yu, R. Motwani, & V. Kumar (Eds.), Next Generation Data Mining, pp. 131-150, Chapman & Hall / Crc.

• D. Malerba, A. Lanza, & A. Appice (2009). Leveraging the power of spatial data mining to enhance the applicability of GIS technology. Chapter 10 in J. Han & R. Cohen (Eds.), Geographic Knowledge Discovery and Data Mining. 2nd Edition, pp. 258-291, CRC Press - Taylor and Francis.

• M. Berardi, D. Malerba, R. Piredda, M. Attimonelli, G. Scioscia & P. Leo (2008). Biomedical Literature Mining for Biological Databases Annotation. Chapter 16 in E.G. Giannopoulou (Ed.), Data Mining in Medical and Biological Research, pp. 267-290, IN-TECH Publisher: Vienna.

• D. Malerba, M. Ceci, & M. Berardi (2008). Machine Learning for Reading Order Detection in Document Image Understanding. In S. Marinai & H. Fujisawa (Eds.), Database Support for Data Mining Applications, Studies in Computational Intelligence, pp. 45-69, Springer-Verlag: Berlin.

• D. Malerba, F. Esposito, & A. Appice (2008). Exporting symbolic objects to databases. Chapter 3 in E. Diday & M. Noirhomme-Fraiture (Eds.), Symbolic Data Analysis and the SODAS Software, pp. 123-148, John Wiley & Sons: Chichester.

• F. Esposito, D. Malerba, & A. Appice (2008). Dissimilarity and matching. Chapter 8 in E. Diday & M. Noirhomme-Fraiture (Eds.), Symbolic Data Analysis and the SODAS Software, pp. 61-66, John Wiley & Sons: Chichester.

• D. Malerba, A. Appice, & M. Ceci (2004). A Data Mining Query Language for Knowledge Discovery in a Geographical Information System. Chapter 5 in R. Meo, P. Lanzi, & M. Klemettinen (Eds.), Machine Learning in Document Analysis and Recognition, LNCS 2682, pp. 95-116, Springer-Verlag: Berlin.

• D. Malerba, F. Esposito, A. Lanza, & F.A. Lisi (2001). Machine learning for information extraction from topographic maps. In H. J. Miller & J. Han (Eds.), Geographic Data Mining and Knowledge Discovery, 291-314, Taylor and Francis, London, UK.

• F. Esposito, D. Malerba, & F.A. Lisi (2000). Matching Symbolic Objects. Chapter 8.4 in in H.-H. Bock and E. Diday (Eds.), Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, Springer-Verlag:Berlin, 186-197.

• F. Esposito, D. Malerba, & V. Tamma (2000). Dissimilarity Measures for Symbolic Objects. Chapter 8.3 in in H.-H. Bock and E. Diday (Eds.), Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, Springer-Verlag:Berlin, 165-185.

• F. Esposito, D. Malerba, V. Tamma, & H.-H. Bock (2000). Classical resemblance measures. Chapter 8.1 in in H.-H. Bock and E. Diday (Eds.), Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, Springer-Verlag:Berlin, 139-152.

• M.F. Costabile, D. Malerba, M. Hemmje, & A. Paradiso (1998). Building metaphors for supporting user interaction in multimedia databases, Chapter 3 in Y. Ioannides and W. Klas (Eds.), Visual Database Systems 4 (VDB4), 47-65, Chapman & Hall, London.

• D. Malerba, G. Semeraro and F. Esposito (1997). A Multistrategy Approach to Learning Multiple Dependent Concepts. Chapter 4 in C.,Taylor and R., Nakhaeizadeh (Eds.), Machine Learning and Statistics: The Interface, pp. 87-106, Wiley, London, England.

   

  Papers in International Collections

• A. Appice, M. Ceci, C. Malgieri & D. Malerba (2007). Discovering Relational Emerging Patterns. In R. Basili, & M.T. Pazienza (Eds.): AI*IA 2007: Artificial Intelligence and Human-Oriented Computing, LNAI 4733, pp. 206-217, Springer: Berlin.

• M. Ceci, A. Appice & D. Malerba (2007). Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach. In J.N. Kok, J. Koronacki, R. López de Mantaras, S. Matwin, D. Mladenic, & A. Skowron (Eds.): Knowledge Discovery in Databases: PKDD 2007, LNAI 4702, pp. 390-397, Springer: Berlin.

• M. Ceci, A. Appice, N. Barile & D. Malerba (2007). Transductive Learning from Relational Data. In P. Perner (Ed.): Machine Learning and Data Mining in Pattern Recognition, 5th International Conference, MLDM 2007, LNAI 4571, pp. 324-338, Springer: Berlin.

• M. Berardi & D. Malerba (2007). Learning Recursive Patterns for Biomedical Information Extraction. In S. Muggleton, R. Otero, & A. Tamaddoni-Nezhad (Eds.): Inductive Logic Programming: ILP 2006, LNAI 4455, pp. 79–93, Springer: Berlin.

• D. Malerba, M. Ceci & A. Appice (2005). Mining Model Trees from Spatial Data, in A. Jorge, L. Torgo, P. Brazdil, R. Camacho & J. Gama (Eds.),Knowledge Discovery in Databases: PKDD 2005, Lecture Notes in Artificial Intelligence, 3721, pp. 169-180, Springer: Berlin.

• D. Malerba, A. Appice, A. Varlaro & A. Lanza (2005). Spatial Clustering of Structured Objects, in S. Kramer and B. Pfahringer (Eds.), Inductive Logic Programming: ILP 2005, Lecture Notes in Artificial Intelligence, 3625, pp. 227-245, Springer: Berlin.

• C. Caruso, D. Malerba & D. Papagni (2005). Learning the Daily Model of Network Traffic, in M.-S. Hacid, N.V. Murray, Z.W. Ras & S. Tsumoto (Eds.),Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Lecture Notes in Artificial Intelligence, 3488, pp. 131-141, Springer: Berlin.

• A. Appice, M. Berardi, M. Ceci & D. Malerba (2005). Mining and Filtering Multi-level Spatial Association Rules with ARES, in M.-S. Hacid, N.V. Murray, Z.W. Ras & S. Tsumoto (Eds.), Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Lecture Notes in Artificial Intelligence, 3488, pp. 342-353, Springer: Berlin.

• M. Ceci, A. Appice, & D. Malerba (2004). Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach, in J.-F. Boulicaut, F. Esposito, F. Giannotti, & D. Pedreschi (Eds.), Knowledge Discovery in Databases: PKDD 2004, Lecture Notes in Artificial Intelligence, 3202, 99-111, Springer, Berlin, Germany.

• M. Berardi, A. Varlaro, & D. Malerba (2004). On the effect of caching in recursive theory learning, in R. Camacho, R.D. King, & A. Srinivasan Inductive Logic Programming: ILP 2004, Lecture Notes in Artificial Intelligence, 3194, 44-62, Springer, Berlin, Germany.

• M. Berardi, M. Lapi & D. Malerba (2004). An integrated approach for automatic semantic structure extraction in document images in S. Marinai & A. Dengel (Eds.) Document Analysis Systems VI. 6th International Workshop, DAS 2004, Lecture Notes in Computer Science, 3163, 179-190, Springer, Berlin, Germany.

• M. Ceci, A. Appice, & D. Malerba (2003). Comparing Simplification Methods for Model Trees with Regression and Splitting Nodes, in N. Zong, Z.W. Ras, S. Tsumoto, E. Suzuki (Eds.), Foundations of Intelligent Systems, 14th International Symposium, ISMIS'2003, Lecture Notes in Artificial Intelligence, 2871, 49-56, Springer, Berlin, Germany.

• F.A. Lisi & D. Malerba (2003). Ideal Refinement of Descriptions in AL-log, in T. Horvath and A. Yamamoto (Eds.) Inductive Logic Programming ILP'03, Lecture Notes in Artificial Intelligence, 2835, 215-232, Springer, Berlin, Germany.

• A. Appice, M. Ceci, & D. Malerba (2003). Mining Model Trees: A Multi-relational Approach, in T. Horvath and A. Yamamoto (Eds.) Inductive Logic Programming ILP'03, Lecture Notes in Artificial Intelligence, 2835, 4-21, Springer, Berlin, Germany.

• M. Ceci, A. Appice, & D. Malerba (2003). Mr-SBC: a Multi-Relational Naive Bayes Classifier, in N. Lavrac, D. Gamberger, L. Todorovski & H. Blockeel (Eds.), Knowledge Discovery in Databases PKDD 2003, Lecture Notes in Artificial Intelligence, 2838, 95-106, Springer, Berlin, Germany.

• D. Malerba, M. Ceci & M. Berardi (2003). XML and Knowledge Technologies for Semantic-Based Indexing of Paper Documents, in V. Marík, W. Retschitzegger, & O. Stepánková (Eds.) Database and Expert Systems Applications, 14th International Conference, DEXA 2003, Lecture Notes in Computer Science, 2736, 256-265, Springer, Berlin, Germany.

• M. Ceci & D. Malerba (2003). Web-pages Classification into a Hierarchy of Categories, In F. Sebastiani (Ed.), Advances in Information Retrieval Proceedings, Lecture Notes in Computer Science, 2633, 57-72, Springer, Berlin, Germany.

• D. Malerba, F. Esposito, & M. Ceci (2002). Mining HTML pages to support document sharing in a cooperative system, In R. Unland, A. Chaudri, D. Chabane & W. Lindner (Eds.), XML-Based Data Management and Multimedia Engineering - EDBT 2002 Workshops, Lecture Notes in Computer Science, 2490, 190-201, Springer, Berlin, Germany.

• D. Malerba, F. Esposito & O. Altamura (2002). Adaptive layout analysis of document images, in H.-S. Hacid, Z.W. Ras, D.A. Zighed & Y. Kodratoff (Eds.), Foundations of Intelligent Systems, 13^th International Symposium, ISMIS'2002, Lecture Notes in Artificial Intelligence, 2366, 526-534, Springer, Berlin, Germany.

• D. Malerba, A. Appice, M. Ceci & M. Monopoli (2002). Trading-off local versus global effects of regression nodes in model trees, in H.-S. Hacid, Z.W. Ras, D.A. Zighed & Y. Kodratoff (Eds.), Foundations of Intelligent Systems, 13^th International Symposium, ISMIS'2002, Lecture Notes in Artificial Intelligence, 2366, 393-402, Springer, Berlin, Germany.

• A. Lanza, D. Malerba, F.A. Lisi, A. Appice & M. Ceci (2002). Generating Logic Descriptions for the Automated Interpretation of Topographic Maps, in D. Blostein and Y.-B. Kwon (Eds.) Graphics Recognition: Algorithms and Applications, Lecture Notes in Computer Science, 2390, 200-210, Springer, Berlin, Germany.

• D. Malerba, A. Appice, A. Bellino, M. Ceci & D. Pallotta (2001). Stepwise Induction of Model Trees, in F. Esposito (Ed.), AI*IA 2001: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, 2175, Springer, Berlin, Germany.

• D. Malerba & F.A. Lisi (2001). Discovering Associations Between Spatial Objects: An ILP Application, in C. Rouveirol & M. Sebag (Eds.), Inductive Logic Programming, Lecture Notes in Artificial Intelligence, 2157, 156-163, Springer, Berlin, Germany.

• D. Malerba, F. Esposito, A. Lanza & F.A. Lisi (2001). First-order rule induction for the recognition of morphological patterns in topographic maps , In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Artificial Intelligence, 2123, 88-101, Springer, Berlin, Germany.

• F. Esposito, D. Malerba & F.A. Lisi (2000). Induction of recursive theories in the normal ILP setting: issues and solutions, in J. Cussens and A. Frisch (Eds.) Inductive Logic Programming, Lecture Notes in Artificial Intelligence, 1866, 93-111, Springer, Berlin, Germany.

• D. Malerba, F. Esposito, A. Lanza & F.A. Lisi (2000). Discovering Geographic Knowledge: The INGENS System. in Z.W. Ras and S. Ohsuga (Eds.), Foundations of Intelligent Systems, 12th International Symposium, ISMIS'2000, Lecture Notes in Artificial Intelligence, 1932, 40-48, Springer, Berlin, Germany.

• F. Esposito, D. Malerba, L. Di Pace & P. Leo (2000). A Machine Learning Approach to Web Mining, In E. Lamma & P. Mello (Eds.), AI*IA 99: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, 1792, 190-201, Springer, Berlin, Germany.

• F. Esposito, D. Malerba, G. Semeraro, & S. Caggese (1999). Discretization of Continuous-Valued Data in Symbolic Classification Learning, in M. Vichi and O. Opitz (Eds.), Classification and Data Analysis: Theory and Application, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 13, Springer-Verlag:Berlin, 81-88.

• D. Malerba, F. Esposito, G. Semeraro & S. Caggese (1997). Handling Continuous Data in Top-Down Induction of First-Order Rules.AI*IA 97: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, 1321, 24-35, Springer, Berlin, Germany.

• D. Malerba, F. Esposito and G. Semeraro (1996). A Further Comparison of Simplification Methods for Decision-Tree Induction. Chapter 35 in D. Fisher and H.-J.Lenz (Eds.), Learning from Data: AI and Statistics V, Lecture Notes in Statistics, 112, 365-374, Springer-Verlag, Berlin, Germany.

• F. Esposito, D. Malerba & G. Semeraro (1995). Simplifying decision trees by pruning and grafting: New results (Extended abstract). In Nada Lavrac and Stefan Wrobel (Eds.), Machine Learning: ECML-95, Lecture Notes in Artificial Intelligence, 912, 287-290, Springer-Verlag, Berlin, Germany.

• D. Malerba, G. Semeraro, and F. Esposito (1994). An Analytic and Empirical Comparison of Two Methods for Discovering Probabilistic Causal Relationships. In F. Bergadano, and L. De Raedt (Eds.), Machine Learning: ECML-94, Lecture Notes in Artificial Intelligence, 784, 198-216, Berlin:Springer-Verlag.

• F. Esposito, D. Malerba, and G. Semeraro (1993). Decision Tree Pruning as a Search in the State Space. In P. Brazdil (Ed.), Machine Learning: ECML-93, Lecture Notes in Artificial Intelligence, 667, 165-184, Berlin:Springer-Verlag.

   

  Proceedings of International Conferences 

• M. Berardi, M. Ceci, F. Esposito & D. Malerba (2003). Learning Logic Programs for Layout Analysis Correction, Proceedings of the 20^th International Conference on Machine Learning (ICML2003), 27-34.

• D. Malerba, F. Esposito, O. Altamura, M. Ceci & M. Berardi (2003). Correcting the Document Layout: A Machine Learning Approach, Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003), 97-102, IEEE Computer Society Press, Los Vaqueros, CA.

• D. Malerba, F. Esposito & M. Monopoli (2002). Comparing dissimilarity measures for p robabilistic symbolic objects. In A. Zanasi, C. A. Brebbia, N.F.F. Ebecken, P. Melli (Eds.) Data Mining III, Series Management Information Systems, Vol 6, 31-40, WIT Press, Southampton, UK.

• D. Malerba, F. Esposito, V. Gioviale & V. Tamma (2001). Comparing dissimilarity measures in Symbolic Data Analysis. Proceedings of the Joint Conferences on "New Techniques and Technologies for Statistcs" and "Exchange of Technology and Know-how" (ETK-NTTS'01), 473-481.

• D. Malerba, F. Esposito, F.A. Lisi & O. Altamura (2001). Automated Discovery of Dependencies Between Logical Components in Document Image Understanding, Proceedings of the Sixth International Conference on Document Analysis and Recognition, 174-178, IEEE Computer Society Press, Los Vaqueros, CA.

• D. Malerba, F. Esposito & F.A. Lisi (2001). Mining spatial association rules in census data. Proceedings of the Joint Conferences on "New Techniques and Technologies for Statistcs" and "Exchange of Technology and Know-how" (ETK-NTTS'01), 541-550.

• O.Altamura, F. Esposito & D. Malerba (1999). WISDOM++: An Interactive and Adaptive Document Analysis System, Proceedings of the Fifth International Conference on Document Analysis and Recognition, 366-369, IEEE Computer Society Press, Los Vaqueros, CA.

• F. Esposito, D. Malerba & F.A. Lisi (1998). Flexible Matching of Boolean Symbolic Objects. Proceedings of NTTS'98, Int. Seminar on New Techniques & Technologies for Statistics, 157-162, Napoli.

• D. Malerba, F. Esposito & F.A. Lisi (1998). Learning recursive theories with ATRE, in H. Prade (Ed.), Proceedings of the 13^th European Conference on Artificial Intelligence, 435-439, John Wiley & Sons, Chichester, England.

• F. Esposito, D. Malerba, G. Semeraro, C.D. Antifora & G. De Gennaro (1997). Information Capture and Semantic Indexing of Digital Libraries Through Machine Learning Techniques. Proceedings of the Fourth International Conference on Document Analysis and Recognition, 722-727, IEEE Computer Society Press, Los Vaqueros, CA.

• F. Esposito, S. Caggese, D. Malerba & G. Semeraro (1997). Classification in Noisy Domains by Flexible Matching. Proceedings of the European Symposium on Intelligent Techniques, 45-49, Bari.

• F. Esposito, D. Malerba & G. Semeraro (1995). A Knowledge-Based Approach to the Layout Analysis. Proceedings of the Third International Conference on Document Analysis and Recognition, 466-471.IEEE COmputer Society Press, Montreal, Canada.

• D. Malerba, G. Semeraro & E. Bellisari (1995). LEX: A Knowledge-Based System for the Layout Analysis. Proceedings of the Third International Conference on the Practical Application of Prolog, 429-443. Paris. France

   

  Proceedings of National Conferences

• C. D'Amato, D. Malerba, F. Esposito & M. Monopoli (2003). Extending the K-Nearest Neighbour classification algorithm to symbolic objects. Atti del Convegno Intermedio della Società Italiana di Statistica "Analisi Statistica Multivariata per le scienze economico-sociali, le scienze naturali e la tecnologia". Napoli. Italia.

   

  Proceedings of International Workshops 

• D. Malerba, A. Appice, & N. Vacca (2002) SDMOQL: An OQL-based Data Mining Query Language for Map Interpretation Tasks. Proc. of the Workshop on Database Technologies for Data Mining (DTDM'02), in conjunction with the VIII International Conference on Extending Database Technology (EDBT'02), Prague, Czech Republic, March 25-27, 2002

• P. Brito & D. Malerba (Eds.) (2002). Proceedings of the ECML/PKDD Workshop on "Mining Official Data", Helsinki University Printing House, Helsinki, ISBN: 952-10-0637-4.

• F. Esposito & D. Malerba (2000). Proceedings of the ECAI 2000 Workshop on "Machine Learning in Computer Vision", Berlin, Germany.

• P. Brito, J., Costa & D. Malerba (2000). Proceedings of the ECML 2000 Workshop on "Dealing with Structured Data in Machine Learning and Statistics", Barcelona.

• F. Esposito, D. Malerba, L. Di Pace & P. Leo (1999). A Learning Intermediary for Automated Classification of Web Pages, Proc. of the ICML'99 Workshop on Machine Learning in Text Data Analysis, 37-46, Bled, Slovenia.

   

  Demo

• M.F. Costabile, D. Malerba, M. Hemmje & A. Paradiso (1998). Building metaphors for supporting user interaction in multimedia databases - A demonstration , Chapter 10 in Y. Ioannides and W. Klas (Eds.), Visual Database Systems 4 (VDB4), 154-160, Chapman & Hall, London.