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Efficient learning of hierarchical latent class models

Author
ZHANG, Nevin L1 ; KOCKA, Tomao2
[1] Department of Computer Science. Hong Kong University of Science & Technology, Hong-Kong
[2] Laboratory of Intelligent Systems Prague Prague University of Economics, Prague, Czech Republic
Conference title
16th IEEE international conference on tools with artificial intelligence ICTAI 2004 (Boca Raton Fl, 15-17 November 2004)
Conference name
ICTAI : international conference on tools with artificial intelligence (16 ; Boca Raton FL 2004)
Source

Proceedings - International Conference on Tools with Artificial Intelligence, TAI. 2004 ; 1Vol, pp 585-593, 9 p ; ref : 8 ref

ISSN
1082-3409
ISBN
0-7695-2236-X
Scientific domain
Computer science
Publisher
IEEE, Los Alamitos CA
Publication country
United States
Document type
Conference Paper
Language
English
Keyword (fr)
Algorithme recherche Complexité calcul Extensibilité Intelligence artificielle Modèle donnée Modélisation Réseau Bayes Structure arborescente Système hiérarchisé
Keyword (en)
Search algorithm Computational complexity Scalability Artificial intelligence Data models Modeling Bayes network Tree structure Hierarchical system
Keyword (es)
Algoritmo búsqueda Complejidad computación Estensibilidad Inteligencia artificial Modelización Red Bayes Estructura arborescente Sistema jerarquizado
Classification
Pascal
001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02C Artificial intelligence

Discipline
Computer science : theoretical automation and systems
Origin
Inist-CNRS
Database
PASCAL
INIST identifier
19103888

Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS

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