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Machine inductive inference and language identification
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-08353-7_182openalex-snapshot
Identification of formal languages
inferredcites →https://doi.org/10.1007/3-540-10854-8_9openalex-snapshot
On the error correcting power of pluralism in inductive inference
inferredcites →https://doi.org/10.1007/bf01761704openalex-snapshot
Periodicity in generations of automata
inferredcites →https://doi.org/10.1016/0010-0277(79)90001-5openalex-snapshot
Formal models of language learning
inferredcites →https://doi.org/10.1016/0010-0277(82)90005-1openalex-snapshot
A note on formal learning theory
inferredcites →https://doi.org/10.1016/0010-0277(82)90006-3openalex-snapshot
On extensional learnability
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Finding patterns common to a set of strings
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Introduction to automata theory, languages, and computation
inferredcites →https://doi.org/10.1016/0304-3975(76)90087-6openalex-snapshot
Some natural properties of strong-identification in inductive inference
inferredcites →https://doi.org/10.1016/0304-3975(83)90061-0openalex-snapshot
Comparison of identification criteria for machine inductive inference
inferredcites →https://doi.org/10.1016/0378-4754(79)90040-5openalex-snapshot
An introduction to the general theory of algorithms
inferredcites →https://doi.org/10.1016/0378-4754(81)90068-9openalex-snapshot
Introduction to automata theory, languages and computation
inferredcites →https://doi.org/10.1016/s0019-9958(67)91165-5openalex-snapshot
Language identification in the limit
inferredcites →https://doi.org/10.1016/s0019-9958(72)90424-xopenalex-snapshot
Some decidability results on grammatical inference and complexity
inferredcites →https://doi.org/10.1016/s0019-9958(75)90261-2openalex-snapshot
Toward a mathematical theory of inductive inference
inferredcites →https://doi.org/10.1016/s0019-9958(80)90285-5openalex-snapshot
Inductive inference of formal languages from positive data
inferredcites →https://doi.org/10.1016/s0019-9958(82)80025-9openalex-snapshot
Criteria of language learning
inferredcites →https://doi.org/10.1016/s0019-9958(82)80086-7openalex-snapshot
Tradeoffs in the inductive inference of nearly minimal size programs
inferredcites →https://doi.org/10.1016/s0020-0255(80)80006-5openalex-snapshot
Research in the theory of inductive inference by GDR mathematicians—A survey
inferredcites →https://doi.org/10.1093/comjnl/12.1.28openalex-snapshot
Theory of Recursive Functions and Effective Computability
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The power of parallelism for automatic program synthesis
inferredcites →https://doi.org/10.1145/322326.322334openalex-snapshot
Inference of Reversible Languages
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Anomaly hierarchies of mechanized inductive inference
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Theory of Recursive Functions and Effective Computability.
inferredcites →https://doi.org/10.2307/2964292openalex-snapshot
Gödel numberings of partial recursive functions
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Formal principles of language acquisition . By Kenneth Wexler and Peter W. Culicover. Cambridge, MA: MIT Press, 1980. Pp. xvii, 647. $35.00.
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Model Identification and Synthesis of Discrete‐Event Systems
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Learning with the Knowledge of an Upper Bound on Program Size
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Characterizations of Monotonic and Dual Monotonic Language Learning
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Computational Limits on Team Identification of Languages
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The Synthesis of Language Learners
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Language Learning with Some Negative Information
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Maximal Machine Learnable Classes
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Inductive Inference with Additional Information
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Synthesizing Learners Tolerating Computable Noisy Data
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Language Learning from Texts: Degrees of Intrinsic Complexity and Their Characterizations
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Gold-Style and Query Learning Under Various Constraints on the Target Class
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Non U-Shaped Vacillatory and Team Learning
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Learning Multiple Languages in Groups
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Some Recent Results in U-Shaped Learning
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On Learning Languages from Positive Data and a Limited Number of Short Counterexamples
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Learning and Extending Sublanguages
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Iterative Learning from Positive Data and Negative Counterexamples
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On the inference of programs approximately computing the desired function
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A Negative Result on Inductive Inference of Extended Pattern Languages
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Learning Recursive Concepts with Anomalies
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Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
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Synthesizing Learners Tolerating Computable Noisy Data
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A unifying approach to monotonic language learning on informant
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Learning from multiple sources of inaccurate data
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Language learning with some negative information
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Language learning with a bounded number of mind changes
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Learnability: Admissible, co-finite, and hypersimple languages
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Inductive inference with bounded mind changes
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Prudence in vacillatory language identification (Extended abstract)
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On aggregating teams of learning machines
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Learning with higher order additional information
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Language learning under various types of constraint combinations
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A guided tour across the boundaries of learning recursive languages
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On identification by teams and probabilistic machines
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Vacillatory and BC learning on noisy data
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Learning of R.E. Languages from good examples
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Synthesizing noise-tolerant language learners
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Connections Between Inductive Inference and Machine Learning
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Directions for Computability Theory Beyond Pure Mathematical
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Introduction to the Philosophy and Mathematics of Algorithmic Learning Theory
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Inductive Inference Systems for Learning Classes of Algorithmically Generated Sets and Structures
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Connections Between Inductive Inference and Machine Learning
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Connections Between Inductive Inference and Machine Learning
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Learnability and Positive Equivalence Relations
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Mapping Monotonic Restrictions in Inductive Inference
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Normal Forms for Semantically Witness-Based Learners in Inductive Inference
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Learning Languages with Decidable Hypotheses
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Maps of Restrictions for Behaviourally Correct Learning
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On the Role of Update Constraints and Text-Types in Iterative Learning
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Learning from Positive Data and Negative Counterexamples: A Survey
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Combining Models of Approximation with Partial Learning
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Learning Finite Variants of Single Languages from Informant
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Intrinsic Complexity of Partial Learning
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Learning Pattern Languages over Groups
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Learners Based on Transducers
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Replacing Limit Learners with Equally Powerful One-Shot Query Learners
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Learning Languages from Positive Data and Negative Counterexamples
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Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space
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Learning Languages from Positive Data and a Finite Number of Queries
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U-Shaped, Iterative, and Iterative-with-Counter Learning
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Learning Correction Grammars
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Resource Restricted Computability Theoretic Learning: Illustrative Topics and Problems
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Learning in Friedberg Numberings
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Parallelism Increases Iterative Learning Power
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Prescribed Learning of R.E. Classes
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Iterative Learning of Simple External Contextual Languages
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Hypothesis Spaces for Learning
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Iterative Learning from Texts and Counterexamples Using Additional Information
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Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations
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Inductive Inference of Languages from Samplings
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Learning Families of Closed Sets in Matroids
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Learnability of Co-r.e. Classes
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Automatic Learning from Positive Data and Negative Counterexamples
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On Conservative Learning of Recursively Enumerable Languages
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Topological Separations in Inductive Inference
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Partial Learning of Recursively Enumerable Languages
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Gold-Style Learning Theory
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Prudence in vacillatory language identification
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Machine learning of higher order programs
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Monotonic versus non-monotonic language learning
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Unions of identifiable families of languages
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Resource Restricted Computability Theoretic Learning: Illustrative Topics and Problems
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Input-Dependence in Function-Learning
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U-shaped, iterative, and iterative-with-counter learning
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Iterative learning from texts and counterexamples using additional information
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On the non-existence of maximal inference degrees for language identification
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Note on a central lemma for learning theory
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On the inference of approximate programs
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On aggregating teams of learning machines
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Program size restrictions in computational learning
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Learning in the presence of inaccurate information
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Monotonic and dual monotonic language learning
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Prudence and other conditions on formal language learning
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Learning in the presence of partial explanations
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Learning in the Presence of Inaccurate Information
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Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines
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Inductive inference and reverse mathematics
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Relations between Gold-style learning and query learning
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Learning languages from positive data and a finite number of queries
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Variations on U-shaped learning
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Results on memory-limited U-shaped learning
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Iterative learning from positive data and negative counterexamples
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Learning in Friedberg numberings
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Hypothesis spaces for learning
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On the role of update constraints and text-types in iterative learning
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Strongly non-U-shaped language learning results by general techniques
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Learnability and positive equivalence relations
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Incremental learning of approximations from positive data
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Learning languages from positive data and negative counterexamples
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Non-U-shaped vacillatory and team learning
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Inductive inference of approximations for recursive concepts
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A general comparison of language learning from examples and from queries
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Learning multiple languages in groups
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Learning languages from positive data and a limited number of short counterexamples
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Learning indexed families of recursive languages from positive data: A survey
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Learning and extending sublanguages
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Prescribed learning of r.e. classes
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Parallelism increases iterative learning power
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Iterative learning of simple external contextual languages
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Memory-limited non-U-shaped learning with solved open problems
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Mind change speed-up for learning languages from positive data
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Partial learning of recursively enumerable languages
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Topological separations in inductive inference
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Learning pattern languages over groups
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Inductive inference of approximations
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Intrinsic complexity of learning geometrical concepts from positive data
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Vacillatory learning of nearly minimal size grammars
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Open problems in “systems that learn”
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Characterizing language identification in terms of computable numberings
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On the learnability of recursively enumerable languages from good examples
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Synthesizing noise-tolerant language learners
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Learning algebraic structures from text
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Closedness properties in ex-identification
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Probabilistic language learning under monotonicity constraints
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Vacillatory and BC learning on noisy data
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Learning by switching type of information
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Characterization of language learning front informant under various monotonicity constraints
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Strong monotonic and set-driven inductive inference
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On the Necessity of U‐Shaped Learning
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Learning from Multiple Sources of Inaccurate Data
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The Power of Vacillation in Language Learning
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NEGATIVE DATA IN LEARNING LANGUAGES
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Types of monotonic language learning and their characterization
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Probability is more powerful than team for language identification from positive data
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On the intrinsic complexity of language identification
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Synthesizing enumeration techniques for language learning
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Learnability of a subclass of extended pattern languages
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Extensional set learning (extended abstract)
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Inductive Inference: Theory and Methods
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Probabilistic inductive inference
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Uma nova formulação algébrica para o autômato finito adaptativo de segunda ordem aplicada a um modelo de inferência indutiva.
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Robust separations in inductive inference
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Machine learning of higher-order programs
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Maps for learning indexable classes
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Learning from Informants: Relations between Learning Success Criteria
inferred← citeshttps://doi.org/10.5109/13443openalex-snapshot
TRADING MONOTONICITY DEMANDS VERSUS EFFICIENCY
inferred← citeshttps://doi.org/10.5555/93335.93354openalex-snapshot
Learning in the presence of inaccurate information
inferred← citeshttps://doi.org/10.5555/93335.93355openalex-snapshot
Convergence to nearly minimal size grammars by vacillating learning machines
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