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
fDate :
27 Jun-1 Jul 1994
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;
Conference_Titel :
Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on
Conference_Location :
Trondheim
Print_ISBN :
0-7803-2015-8
DOI :
10.1109/ISIT.1994.394627
