DocumentCode :
889554
Title :
Learning subsequential transducers for pattern recognition interpretation tasks
Author :
Oncina, José ; García, Pedro ; Vidal, Enrique
Author_Institution :
Dept. de Sistemas Inf. y Comput., Univ. Politecnica de Valencia, Spain
Volume :
15
Issue :
5
fYear :
1993
fDate :
5/1/1993 12:00:00 AM
Firstpage :
448
Lastpage :
458
Abstract :
A formalization of the transducer learning problem and an effective and efficient method for the inductive learning of an important class of transducers, the class of subsequential transducers, are presented. The capabilities of subsequential transductions are illustrated through a series of experiments that also show the high effectiveness of the proposed learning method in obtaining very accurate and compact transducers for the corresponding tasks
Keywords :
inference mechanisms; learning (artificial intelligence); pattern recognition; formalization; inductive learning; inference; learning; pattern recognition; subsequential transducers; Learning systems; Pattern recognition; Samarium; Testing; Transducers;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.211465
Filename :
211465
Link To Document :
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