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How considering incompatible state mergings may reduce the DFA induction search tree

https://doi.org/10.1007/bfb0054076
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-58473-0_134openalex-snapshot
What is the search space of the regular inference?
inferredcites →https://doi.org/10.1007/3-540-58473-0_152openalex-snapshot
Regular grammatical inference from positive and negative samples by genetic search: the GIG method
inferredcites →https://doi.org/10.1007/bf00058754openalex-snapshot
A Version Space Approach to Learning Context-free Grammars
inferredcites →https://doi.org/10.1007/bf00116251openalex-snapshot
Induction of Decision Trees
inferredcites →https://doi.org/10.1007/bfb0033365openalex-snapshot
Identification of DFA: Data-dependent versus data-independent algorithms
inferredcites →https://doi.org/10.1007/bfb0054059openalex-snapshot
Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm
inferredcites →https://doi.org/10.1016/0004-3702(82)90040-6openalex-snapshot
Generalization as search
inferredcites →https://doi.org/10.1016/0096-0551(78)90006-1openalex-snapshot
A solution of the syntactical induction-inference problem for regular languages
inferredcites →https://doi.org/10.1016/s0019-9958(78)90683-6openalex-snapshot
On the complexity of minimum inference of regular sets
inferredcites →https://doi.org/10.1038/1801097d0openalex-snapshot
Information and Control
inferredcites →https://doi.org/10.1142/0580openalex-snapshot
Syntactic and Structural Pattern Recognition — Theory and Applications
inferredcites →https://doi.org/10.1142/2729openalex-snapshot
Shape, Structure and Pattern Recognition
inferredcites →https://doi.org/10.1142/9789812797902_0004openalex-snapshot
INFERRING REGULAR LANGUAGES IN POLYNOMIAL UPDATED TIME
inferredcites →https://doi.org/10.1142/9789812814913_0009openalex-snapshot
GRAMMATICAL INFERENCE
inferredcites →https://doi.org/10.1142/9789814532860openalex-snapshot
Shape, Structure and Pattern Recognition
inferredcites →https://doi.org/10.1145/130385.130390openalex-snapshot
Random DFA's can be approximately learned from sparse uniform examples
inferredcites →https://doi.org/10.1145/174644.174647openalex-snapshot
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
inferredcites →https://doi.org/10.1145/73007.73049openalex-snapshot
Crytographic limitations on learning Boolean formulae and finite automata

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/3-540-45790-9_3openalex-snapshot
Inferring Subclasses of Regular Languages Faster Using RPNI and Forbidden Configurations
inferred← citeshttps://doi.org/10.1007/978-3-030-13435-8_12openalex-snapshot
Efficient Symmetry Breaking for SAT-Based Minimum DFA Inference
inferred← citeshttps://doi.org/10.1007/978-3-031-71177-0_4openalex-snapshot
DFAMiner: Mining Minimal Separating DFAs from Labelled Samples
inferred← citeshttps://doi.org/10.1007/978-3-540-88009-7_11openalex-snapshot
State-Merging DFA Induction Algorithms with Mandatory Merge Constraints
inferred← citeshttps://doi.org/10.1007/978-981-95-7826-9_10openalex-snapshot
Efficient Decomposition Identification of Deterministic Finite Automata from Examples
inferred← citeshttps://doi.org/10.1007/s10009-019-00544-0openalex-snapshot
Learning Moore machines from input–output traces
inferred← citeshttps://doi.org/10.1016/j.patcog.2004.03.027openalex-snapshot
Inference of regular languages using state merging algorithms with search
inferred← citeshttps://doi.org/10.1023/a:1010828029885openalex-snapshot
Efficient Algorithms for the Inference of Minimum Size DFAs
inferred← citeshttps://doi.org/10.1080/08839510701853200openalex-snapshot
THE QSM ALGORITHM AND ITS APPLICATION TO SOFTWARE BEHAVIOR MODEL INDUCTION
inferred← citeshttps://doi.org/10.17586/2226-1494-2020-20-3-394-401openalex-snapshot
Deterministic finite automata learning using counterexample guided abstraction refinement
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