Incremental induction of rules for document image understanding

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2829, Issue , 2003, Pages 176-188

  Ferilli, Stefano ^a   Di Mauro, Nicola ^a   Basile, Teresa M A ^a   Esposito, Floriana ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

FORMAL LOGIC; KNOWLEDGE ENGINEERING; MACHINE COMPONENTS; SEMANTICS; IMAGE UNDERSTANDING; LEARNING SYSTEMS; STANDARDIZATION; STANDARDS; STRATEGIC PLANNING;

CULTURAL HERITAGES; FIRST ORDER LOGIC; FORMATTING STANDARDS; INCREMENTAL INDUCTION; INCREMENTAL LEARNING; SCIENTIFIC PAPERS; SEMANTIC ROLES;

LEARNING SYSTEMS; LOGIC PROGRAMMING;

DOCUMENT DOMAINS; LOGICAL COMPONENTS; MULTISTRATEGIES; SEMANTIC ROLE;

EID: 7444244864     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39853-0_15     Document Type: Conference Paper

References (12)

1. [1] J.M. Becker. Inductive learning of decision rules with exceptions: Methodology and experimentation. B.s. diss., Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, 1985. UIUCDCS-F-85-945.
2. [2] Thomas G. Dietterich. Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation, 10(7):1895–1923, 1998.
3. [3] F. Esposito, D. Malerba, and F.A. Lisi. Machine learning for intelligent processing of printed documents. Journal of Intelligent Information Systems, 14(2/3):175–198, 2000.
4. [4] 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.
5. [5] E. Lamma, P. Mello, F. Riguzzi, F. Esposito, S. Ferilli, and G. Semeraro. Cooperation of abduction and induction in logic programming. In A.C. Kakas and P. Flach, editors, Abductive and Inductive Reasoning: Essays on their Relation and Integration. Kluwer, 2000.
6. [6] J.W. Lloyd. Foundations of Logic Programming. Springer-Verlag, Berlin, second edition, 1987.
7. [7] R. S. Michalski. Inferential theory of learning. developing foundations for multistrategy learning. In R. S. Michalski and G. Tecuci, editors, Machine Learning. A Multistrategy Approach, volume IV, pages 3–61. Morgan Kaufmann, San Mateo, CA, U. S.A., 1994.
8. [8] S. Muggleton. Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3-4):245–286, 1995.
9. [9] G.D. Plotkin. A note on inductive generalization. Machine Intelligence, 5:153–163, 1970.
10. [10] G. Semeraro, F. Esposito, D. Malerba, N. Fanizzi, and S. Ferilli. A logic framework for the incremental inductive synthesis of datalog theories. In N.E. Fuchs, editor, Logic Program Synthesis and Transformation, number 1463 in Lecture Notes in Computer Science, pages 300–321. Springer-Verlag, 1998.
11. [11] S. Wrobel. Concept Formation and Knowledge Revision. Kluwer Academic Publishers, 1994.
12. [12] J.-D. Zucker. Semantic abstraction for concept representation and learning. In R. S. Michalski and L. Saitta, editors, Proceedings of the 4th International Workshop on Multistrategy Learning, Desenzano del Garda, Italy, 1998.