Random DFA's can be approximately learned from sparse uniform examples

Authors:
Kevin J. Lang
Affiliations:
NEC Research Institute, 4 Independence Way, Princeton NJ
Venue:
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Year:
1992

Citing 3
Cited 39

Random number generators: good ones are hard to find

Communications of the ACM
The minimum consistent DFA problem cannot be approximated within and polynomial

STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Crytographic limitations on learning Boolean formulae and finite automata

STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing

Efficient learning of typical finite automata from random walks

STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Analysis of dynamical recognizers

Neural Computation
A new algorithm for the reduction of incompletely specified finite state machines

Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
A sampling-based heuristic for tree search applied to grammar induction

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning functions represented as multiplicity automata

Journal of the ACM (JACM)
Using computational learning strategies as a tool for combinatorial optimization

Annals of Mathematics and Artificial Intelligence
Learning DFA from Simple Examples

Machine Learning
Efficient Algorithms for the Inference of Minimum Size DFAs

Machine Learning
Inferring Subclasses of Regular Languages Faster Using RPNI and Forbidden Configurations

ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Beyond EDSM

ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Inducing grammars from sparse data sets: a survey of algorithms and results

The Journal of Machine Learning Research
A machine learning method for extracting symbolic knowledge from recurrent neural networks

Neural Computation
Learning regular languages using RFSAs

Theoretical Computer Science - Special issue: Algorithmic learning theory
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating Annotated Behavior Models from End-User Scenarios

IEEE Transactions on Software Engineering
Interactive learning of node selecting tree transducer

Machine Learning
Learning DFA representations of HTTP for protecting web applications

Computer Networks: The International Journal of Computer and Telecommunications Networking
The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx

Neural Computation
Improving dynamic software analysis by applying grammar inference principles

Journal of Software Maintenance and Evolution: Research and Practice - Special Issue on Program Comprehension through Dynamic Analysis (PCODA)
Learning Commutative Regular Languages

ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Evaluation and Comparison of Inferred Regular Grammars

ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
THE QSM ALGORITHM AND ITS APPLICATION TO SOFTWARE BEHAVIOR MODEL INDUCTION

Applied Artificial Intelligence
Group-Linking Method: A Unified Benchmark for Machine Learning with Recurrent Neural Network

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Angluin-style learning of NFA

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Regular Model Checking Using Inference of Regular Languages

Electronic Notes in Theoretical Computer Science (ENTCS)
A bibliographical study of grammatical inference

Pattern Recognition
A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference

Pattern Recognition
Inference of regular languages using state merging algorithms with search

Pattern Recognition
Learning meets verification

FMCO'06 Proceedings of the 5th international conference on Formal methods for components and objects
Learning automata teams

ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
CGE: a sequential learning algorithm for mealy automata

ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Lower bounds on learning random structures with statistical queries

ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Learning finite cover automata from queries

Journal of Computer and System Sciences
A similar fragments merging approach to learn automata on proteins

ECML'05 Proceedings of the 16th European conference on Machine Learning
Inferring network invariants automatically

IJCAR'06 Proceedings of the Third international joint conference on Automated Reasoning
Polynomial characteristic sets for DFA identification

Theoretical Computer Science
An incremental learning algorithm for extended mealy automata

ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
STAMINA: a competition to encourage the development and assessment of software model inference techniques

Empirical Software Engineering
Guided GUI testing of android apps with minimal restart and approximate learning

Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Approximate inference of finite state machines from sparse labeled examples has been proved NP-hard when an adversary chooses the target machine and the training set [Ang78, KV89, PW89]. We have, however, empirically found that DFA's are approximately learnable from sparse data when the target machine and training set are selected at random.