Extracting minimum length Document Type definitions is NP-hard

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3264, Issue , 2004, Pages 277-278

  Fernau, Henning ^a,b  

^a UNIVERSITY OF NEWCASTLE   (Australia)

^b UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; AUTOMATA THEORY; COMPUTATIONAL COMPLEXITY; ENCODING (SYMBOLS); FORMAL LANGUAGES; LEARNING ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING; SUBROUTINES;

DOCUMENT TYPE DESCRIPTORS (DTD); GRAMMATICAL INFERENCE; MINIMUM DESCRIPTION LENGTH (MDL); OCCAM'S RAZOR; REGULAR EXPRESSIONS (RE);

XML;

EID: 22944457548     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30195-0_26     Document Type: Conference Paper

References (3)

1. J. Berstel and L. Boasson. XML grammars. Acta Informatica 38:649-671, 2002.
2. H. Fernau. Learning XML grammars. In P. Perner, editor, Machine Learning and Data Mining in Pattern Recognition MLDM'01, volume 2123 of LNCS/LNAI, pages 73-87. Springer, 2001.
3. M. Garofalakis, A. Gionis, R. Rastogi, S. Seshadri, and K. Shim. XTRACT: learning document type descriptors from XML document collections. Data Mining and Knowledge Discovery, 7:23-56, 2003.