DocumentCode
2617868
Title
Inferring probabilistic acyclic automata using the minimum description length principle
Author
Singer, Yoram ; Tishby, Naftali
Author_Institution
Inst. of Comput. Sci., Hebrew Univ., Jerusalem, Israel
fYear
1994
fDate
27 Jun-1 Jul 1994
Firstpage
392
Abstract
The use of Rissanen´s (1978) minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems
Keywords
inference mechanisms; learning (artificial intelligence); pattern recognition; probabilistic automata; adaptive dimension; adaptive structure; learning algorithm; minimum description length principle; pattern recognition problems; probabilistic acyclic automata; synthetic data; Computer science; Handwriting recognition; Hidden Markov models; Learning automata; Pattern recognition; Probability distribution; Speech; Testing; Topology; Transmitters;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on
Conference_Location
Trondheim
Print_ISBN
0-7803-2015-8
Type
conf
DOI
10.1109/ISIT.1994.394627
Filename
394627
Link To Document