PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance
UNSPECIFIED (2005) PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance. In: 16th Annual International Conference on Algorithmic Learning Theory (LAT 2005), Singapore, SINGAPORE, OCT 08-11, 2005. Published in: ALGORITHMIC LEARNING THEORY, 3734 pp. 157-170.Full text not available from this repository.
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs axe a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Series Name:||LECTURE NOTES IN ARTIFICIAL INTELLIGENCE|
|Journal or Publication Title:||ALGORITHMIC LEARNING THEORY|
|Editor:||Jain, S and Simon, HU and Tomita, E|
|Number of Pages:||14|
|Page Range:||pp. 157-170|
|Title of Event:||16th Annual International Conference on Algorithmic Learning Theory (LAT 2005)|
|Location of Event:||Singapore, SINGAPORE|
|Date(s) of Event:||OCT 08-11, 2005|
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