PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Probabilistic Finite Automata
Colin de la Higuera and Jose Oncina
Lecture Notes in Artificial Intelligence 2004. ISSN 0302-9743

Abstract

Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of these finite state machines. In the setting of identification in the limit with probability one, we prove that stochastic deterministic finite automata cannot be identified from only a polynomial quantity of data. If concerned with approximation results, they become \textsc{Pac}-learnable if the $L_{\infty}$ norm is used. We also investigate queries that are sufficient for the class to be learnable.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:141
Deposited By:Jose Oncina
Deposited On:31 May 2004