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Journal ArticleDOI

Inference of Reversible Languages

Dana Angluin
- 01 Jul 1982 - 
- Vol. 29, Iss: 3, pp 741-765
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TLDR
An efficient algonthrn is presented for mfernng reversible languages from posmve and negative examples, and it is shown that it leads to correct identification m the hmlt of the class of reversible languages.
Abstract
A famdy of efficient algorithms for referring certain subclasses of the regular languages from fmtte posttwe samples is presented These subclasses are the k-reversible languages, for k = 0, 1, 2, . . . . For each k there is an algorithm for finding the smallest k-reversible language containing any fimte posluve sample. It ts shown how to use this algorithm to do correct identification m the ILmlt of the kreversible languages from posmve data A reversible language is one that Is k-reverstble for some k __ 0. An efficient algonthrn is presented for mfernng reversible languages from posmve and negative examples, and it is shown that it leads to correct identification m the hmlt of the class of reversible languages. Numerous examples are gtven to dlustrate the algorithms and their behawor

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Foundations of Machine Learning

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Inductive Inference: Theory and Methods

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Identifying hierarchical structure in sequences: a linear-time algorithm

TL;DR: SEQUITUR as mentioned in this paper is an algorithm that infers a hierarchical structure from a sequence of discrete symbols by replacing repeated phrases with a grammatical rule that generates the phrase, and continuing this process recursively.
References
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Book

Introduction to Automata Theory, Languages, and Computation

TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
Book

The Design and Analysis of Computer Algorithms

TL;DR: This text introduces the basic data structures and programming techniques often used in efficient algorithms, and covers use of lists, push-down stacks, queues, trees, and graphs.
Journal ArticleDOI

Language identification in the limit

TL;DR: It was found that theclass of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learningable from a text.
Book

Introduction to formal language theory

TL;DR: This volume intended to serve as a text for upper undergraduate and graduate level students and special emphasis is given to the role of algebraic techniques in formal language theory through a chapter devoted to the fixed point approach to the analysis of context-free languages.