Person: Donato Malerba

DBLP: Malerba:Donato

Contributed to:

2012

2012

2010

2010

2009

2007

2007

2005

2003

2003

2003

2003

2001

2001

1999

1999

1997

1997

1997

1995

1995

1994

1993

1993

Wrote 28 papers:

MLDM-2012-CeciAVMPG #classification #paradigm #relational

Transductive Relational Classification in the Co-training Paradigm (MC, AA, HLV, DM, EP, HG), pp. 11–25.

MLDM-2012-MacchiaCM #mining #modelling #network #ranking

Mining Ranking Models from Dynamic Network Data (LM, MC, DM), pp. 566–577.

SAC-2012-GuccioneCAMM #clustering #network

Trend cluster based interpolation everywhere in a sensor network (PG, AC, AA, DM, AM), pp. 827–828.

CIKM-2010-WeningerFHM #database #web

Mapping web pages to database records via link paths (TW, FF, JH, DM), pp. 1637–1640.

SAC-2010-AppiceCM #learning

Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.

SAC-2010-CeciALM #approach #data mining #mining #ranking #relational

Complex objects ranking: a relational data mining approach (MC, AA, CL, DM), pp. 1071–1077.

MLDM-2009-CeciALCFVM #data type #detection #mining #relational

Relational Frequent Patterns Mining for Novelty Detection from Data Streams (MC, AA, CL, CC, FF, CV, DM), pp. 427–439.

MLDM-2009-LoglisciM #mining #multi

Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies (CL, DM), pp. 251–265.

ICDAR-2007-CeciBPM #approach #data mining #detection #mining #order

A Data Mining Approach to Reading Order Detection (MC, MB, GP, DM), pp. 924–928.

MLDM-2007-CeciABM #learning #relational

Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.

ICDAR-2005-BerardiACM #analysis #layout #process

A color-based layout analysis to process censorship cards of film archives (MB, OA, MC, DM), pp. 1110–1114.

ICDAR-2005-CeciBM #comprehension #documentation #image #learning #logic #relational #statistics

Relational Learning techniques for Document Image Understanding: Comparing Statistical and Logical approaches (MC, MB, DM), pp. 473–477.

ECIR-2003-CeciM #classification #documentation #html

Hierarchical Classification of HTML Documents with WebClassII (MC, DM), pp. 57–72.

ICDAR-2003-MalerbaEACB #approach #documentation #layout #machine learning

Correcting the Document Layout: A Machine Learning Approach (DM, FE, OA, MC, MB), p. 97–?.

ICML-2003-BerardiCEM #analysis #layout #learning #logic programming #source code

Learning Logic Programs for Layout Analysis Correction (MB, MC, FE, DM), pp. 27–34.

MLDM-2003-CeciAM

Simplification Methods for Model Trees with Regression and Splitting Nodes (MC, AA, DM), pp. 20–34.

ICDAR-2001-MalerbaELA #automation #component #comprehension #dependence #documentation #image #logic

Automated Discovery of Dependencies Between Logical Components in Document Image Understanding (DM, FE, FAL, OA), pp. 174–178.

MLDM-2001-MalerbaELL #first-order #induction #recognition

First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps (DM, FE, AL, FAL), pp. 88–101.

ICDAR-1999-AltamuraEM #adaptation #analysis #documentation #interactive

WISDOM++: An Interactive and Adaptive Document Analysis System (OA, FE, DM), pp. 366–369.

MLDM-1999-AltamuraELM #documentation #learning

Symbolic Learning Techniques in Paper Document Processing (OA, FE, FAL, DM), pp. 159–173.

ECDL-1997-SemeraroEMFF #library #machine learning #online

Machine Learning + On-line Libraries = IDL (GS, FE, DM, NF, SF), pp. 195–214.

ICDAR-1997-EspositoMSAG #library #machine learning #semantics

Information Capture and Semantic Indexing of Digital Libraries through Machine Learning Techniques (FE, DM, GS, CDA, GdG), pp. 722–727.

LOPSTR-1997-SemeraroEMFF #datalog #framework #incremental #induction #logic #synthesis

A Logic Framework for the Incremental Inductive Synthesis of Datalog Theories (GS, FE, DM, NF, SF), pp. 300–321.

ICDAR-v1-1995-EspositoMS #analysis #approach #knowledge-based #layout

A knowledge-based approach to the layout analysis (FE, DM, GS), pp. 466–471.

LOPSTR-1995-SemeraroEM #datalog #refinement #source code

Ideal Refinement of Datalog Programs (GS, FE, DM), pp. 120–136.

LOPSTR-1994-SemeraroEMBP #case study #learning #logic #source code

Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL (GS, FE, DM, CB, MJP), pp. 183–198.

ICDAR-1993-EspositoMS #automation #comprehension #documentation

Automated acquisition of rules for document understanding (FE, DM, GS), pp. 650–654.

SEKE-1993-EspositoMS #information management #machine learning #refinement

Machine Learning Techniques for Knowledge Acquisition and Refinement (FE, DM, GS), pp. 319–323.