Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Learnability of probabilistic automata via oracles

Date

Authors

Guttman, Omri

Vishwanathan, S

Williamson, Robert

Publisher

Springer

Abstract

Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In

Keywords

Keywords: Algorithms; Learning systems; Probabilistic logics; Theorem proving; Learnability; Oracles; State merging algorithms; Automata theory

URI

http://hdl.handle.net/1885/82367

Collections

ANU Research Publications

Source

Algorithmic Learning Theory: Proceedings of the 16th International Conference on Algorithmic Learning Theory (ALT-05) - LNAI 3734

Type

Conference paper

DOI

10.1007/11564089_15

Restricted until

2037-12-31

Downloads

File

Description

01_Guttman_Learnability_of_probabilistic_2005.pdf (194.4 KB)