F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Improving Automatic Labelling through RDF Management - Esposito, Ferilli, Di.. (2003) Self-citation (Esposito Semeraro Ferilli) (Correct)
No context found.
F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000.
Multistrategy Operators for Relational Learning.. - Esposito.. (2006) Self-citation (Esposito Fanizzi Ferilli) (Correct)
No context found.
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy Theory Revision: Induction and Abduction in INTHELEX, Machine Learning, 38(1/2), 2000, 133--156.
Machine Learning methods for automatically processing.. - Esposito Malerba.. Self-citation (Esposito Semeraro Ferilli) (Correct)
No context found.
Esposito, F., Semeraro, G., Fanizzi, N. & Ferilli, S. (2000). Multistrategy Theory Revision: Induction and Abduction in INTHELEX. Machine Learning Journal, 38(1/2):133-156, Kluwer Academic Publisher.
Automatic Annotation of Historical Paper Documents - Ferilli, Iannone.. Self-citation (Semeraro Ferilli) (Correct)
No context found.
F. Esposito, G. Semeraro, N. Fanizzi & S. Ferilli. Multistrategy Theory Revision: Induction and Abduction in INTHELEX. Machine Learning, 38(1/2):133-156, Kluwer Academic Publ., Boston, January/February 2000.
Subsumption and Resolution: A New - Algorithm Ferilli Di Self-citation (Esposito Ferilli) (Correct)
No context found.
F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000.
Multistrategy Learning of Rules for Automated.. - Semeraro.. (2002) Self-citation (Esposito Fanizzi Ferilli) (Correct)
No context found.
F. Esposito, G.SemA9:#5 N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000.
Incremental Induction of Classification Rules for.. - Basile, Ferilli.. (2004) Self-citation (Esposito Ferilli) (Correct)
No context found.
F. Esposito, G. Semeraro, N. Fanizzi and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000.
Evaluation and Validation of Two Approaches to User.. - Esposito, Semeraro.. Self-citation (Esposito Semeraro Ferilli) (Correct)
No context found.
F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning, 38(1/2):133--156, 2000.
INTHELEX: INcremental THEory Learner from EXamples - Basile, Belsanti, Di Mauro, .. (2001) Self-citation (Ferilli) (Correct)
No context found.
F. Esposito, G. Semeraro, N. Fanizzi & S. Ferilli, Multistrategy theory revision: induction and abduction in INTHELEX, Machine Learning Journal, 38(1/2), 2000, 133-156. Books:
Learning from Parsed Sentences with INTHELEX - Esposito, Ferilli, Fanizzi.. (2000) (2 citations) Self-citation (Esposito Semeraro Fanizzi Ferilli) (Correct)
....of these methods, as a drawback of the expressive power gained through relations, as well as to the possibility that often noise coming from wrongly parsed sentences is present. After presenting in Section 2 the parser, Section 3 shows the results of applying the learning system INTHELEX [5] for the inference of simple events and, lastly, Section 4 outlines future work. 2 A Strati ed Parser for Italian Language This section presents a parser for the Italian language, based on contextfree grammars and designed to manage texts having a simple and standard phrase structure (e.g. ....
....extraction The grammar above was used to parse Italian texts downloaded from the Internet, and concerning foreign commerce. Through such pre processing, the aim was to obtain some structure for those texts that could then be translated in the input language of the learning system INTHELEX [5] in order to make it learn simple events concerning that domain. INTHELEX (INcremental THEory Learner from EXamples) is a fully incremental, multi conceptual closed loop learning system for the induction of hierarchical theories from examples. In detail, full incrementality avoids the need of a ....
F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning, 38(1/2):133{ 156, 2000.
Learning Family Relationships Exploiting Multistrategy - Ferilli, Fanizzi, Basile.. (2002) Self-citation (Fanizzi Ferilli) (Correct)
.... framework for integrating different learning strategies is the Inferential Learning Theory (ILT) 10] All these considerations, plus the need of testing theoretical results on the Object Identity paradigm [6] in practice, led to the design and implementation of the learning system INTHELEX [5]. Its most characterizing features are in its incremental nature, in the reduced need of a deep background knowledge, in the exploitation of negative information, in the peculiar bias on the generalization model, which reduces the search space and does not limit the expressive power of the ....
....to see if and how they are able to enhance the system performance. Finally, Section 4 draws some conclusions and future work. 2 Multistrategical learning in INTHELEX INTHELEX (INcremental THEory Learner from EXamples) is a learning system for the induction of logic theories from examples [5]. Among its characterizing features: it is based on the Object Identity assumption (terms, even variables, denoted by different names within a formula must refer to different objects) it learns theories expressed as sets of Datalog ## clauses [11] from positive and negative examples; it ....
F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000.
Efficient theta-Subsumption under Object Identity - Ferilli, Fanizzi, Di Mauro.. (2002) Self-citation (Fanizzi Ferilli) (Correct)
....and Future Work We proposed a new algorithm for checking # OI subsumption. Preliminary results suggest that it is able to improve the time performance with respect to other state of the art systems. The first prototype of the algorithm, implemented in Prolog, is currently used in INTHELEX [4], a system for inductive learning from examples based on the Object Identity assumption, that is employed in the EU project COLLATE to learn rules for classification and interpretation of historical archive material. The current implementation uses the same search strategy as SLD resolution. ....
F. Esposito, G. Semeraro, N. Fanizzi, and S. Ferilli. Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning Journal, 38(1/2):133--156, 2000.
Extracting Plausible Explanations of Anomalous Data - Will Bridewell And (Correct)
No context found.
Esposito, F.; Semeraro, G.; Fanizzi, N.; and Ferilli, S. 2000. Multistrategy theory revision: Induction and abduction in inthelex. Machine Learning 38(1--2):133--156.
Learning Recursive Theories in the Normal ILP Setting - Malerba (2003) (1 citation) (Correct)
No context found.
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy Theory Revision: Induction and Abduction in INTHELEX, Machine Learning Journal, 38(1/2), 2000, 133--156.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC