• Publications
  • Influence
Learning Regular Sets from Queries and Counterexamples
  • D. Angluin
  • Computer Science, Mathematics
  • Inf. Comput.
  • 1 November 1987
TLDR
The problem of identifying an unknown regular set from examples of its members and nonmembers is addressed. Expand
  • 1,965
  • 311
  • PDF
Queries and concept learning
TLDR
We consider the problem of using queries to learn an unknown concept. Expand
  • 1,079
  • 158
  • PDF
Finding Patterns Common to a Set of Strings
  • D. Angluin
  • Computer Science, Mathematics
  • J. Comput. Syst. Sci.
  • 1 August 1980
TLDR
In this paper, the computational problem of finding a pattern descriptive of a given sample is studied and a polynomial-time algorithm is proposed to solve the problem. Expand
  • 612
  • 132
Inductive Inference of Formal Languages from Positive Data
  • D. Angluin
  • Computer Science, Mathematics
  • Inf. Control.
  • 1 May 1980
TLDR
We consider inductive inference of formal languages, as defined by Gold (1967) , in the case of positive data, i.e., when the examples of a given language are successive elements of some arbitrary enumeration of the elements of the language. Expand
  • 833
  • 131
Computation in networks of passively mobile finite-state sensors
TLDR
The computational power of networks of small resource-limited mobile agents is explored. Expand
  • 504
  • 66
  • PDF
Inference of Reversible Languages
  • D. Angluin
  • Mathematics, Computer Science
  • JACM
  • 1 July 1982
TLDR
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, 3, 4, 5, 6. Expand
  • 530
  • 59
Queries and Concept Learning
We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustivenessExpand
  • 625
  • 55
Learning From Noisy Examples
The basic question addressed in this paper is: how can a learning algorithm cope with incorrect training examples? Specifically, how can algorithms that produce an “approximately correct”Expand
  • 378
  • 45
  • PDF
Fast Probabilistic Algorithms for Hamiltonian Circuits and Matchings
TLDR
We describe and analyse three simple efficient algorithms with good probabilistic behaviour; two algorithms with run times of O ( n (log n ) 2 ) which almost certainly find directed (undirected) Hamiltonian circuits in random graphs of at least cn log n edges, and an algorithm with a run time of O( n log n ) that almost certainly finds a perfect matching in a random graph. Expand
  • 687
  • 42
Inductive Inference: Theory and Methods
TLDR
This survey highlights and explains the main ideas that have been developed in the study of inductive inference, with special emphasis on the relations between the general theory and the implementations. Expand
  • 919
  • 40
  • PDF
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