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Monotonic and non-monotonic inductive inference of functions and patterns

https://doi.org/10.1007/bfb0023322
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-12920-0_29openalex-snapshot
Polynomial time inference of general pattern languages
inferredcites →https://doi.org/10.1007/bf03037092openalex-snapshot
Monotonic and non-monotonic inductive inference
inferredcites →https://doi.org/10.1007/bfb0015939openalex-snapshot
Algorithmic learning from incomplete information: Principles and problems
inferredcites →https://doi.org/10.1016/0022-0000(80)90041-0openalex-snapshot
Finding patterns common to a set of strings
inferredcites →https://doi.org/10.1016/s0019-9958(67)91165-5openalex-snapshot
Language identification in the limit
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/10.1145/356914.356918openalex-snapshot
Inductive Inference: Theory and Methods
inferredcites →https://doi.org/10.1145/800017.800530openalex-snapshot
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What points at this work

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

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Characterizations of Monotonic and Dual Monotonic Language Learning
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Comparing the Power of Probabilistic Learning and Oracle Identification Under Monotonicity Constraints
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A unifying approach to monotonic language learning on informant
inferred← citeshttps://doi.org/10.1007/3-540-57370-4_39openalex-snapshot
Case-based representation and learning of pattern languages
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A guided tour across the boundaries of learning recursive languages
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Reflecting and self-confident inductive inference machines
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Towards a Map for Incremental Learning in the Limit from Positive and Negative Information
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Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
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A Map of Update Constraints in Inductive Inference
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On the Role of Update Constraints and Text-Types in Iterative Learning
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Topological Separations in Inductive Inference
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Learning Automatic Families of Languages
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A Solution to Wiehagen’s Thesis
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Monotonic and dual monotonic language learning
inferred← citeshttps://doi.org/10.1016/0304-3975(95)91134-copenalex-snapshot
Case-based representation and learning of pattern languages
inferred← citeshttps://doi.org/10.1016/j.ic.2015.12.005openalex-snapshot
On the role of update constraints and text-types in iterative learning
inferred← citeshttps://doi.org/10.1016/j.ic.2022.104913openalex-snapshot
Learnability and positive equivalence relations
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Necessary and sufficient conditions for learning with correction queries
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Learning in the limit with lattice-structured hypothesis spaces
inferred← citeshttps://doi.org/10.1016/j.tcs.2012.10.010openalex-snapshot
Memory-limited non-U-shaped learning with solved open problems
inferred← citeshttps://doi.org/10.1016/j.tcs.2015.10.036openalex-snapshot
Topological separations in inductive inference
inferred← citeshttps://doi.org/10.1016/j.tcs.2016.07.028openalex-snapshot
A map of update constraints in inductive inference
inferred← citeshttps://doi.org/10.1016/s0304-3975(00)00136-5openalex-snapshot
Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries
inferred← citeshttps://doi.org/10.1016/s0304-3975(00)00273-5openalex-snapshot
Aspects of complexity of probabilistic learning under monotonicity constraints
inferred← citeshttps://doi.org/10.1080/09528139408953782openalex-snapshot
Characterization of language learning front informant under various monotonicity constraints
inferred← citeshttps://doi.org/10.1080/09528139408953785openalex-snapshot
Ignoring data may be the only way to learn efficiently
inferred← citeshttps://doi.org/10.1080/095281397147275openalex-snapshot
Strong monotonic and set-driven inductive inference
inferred← citeshttps://doi.org/10.1145/130385.130427openalex-snapshot
Types of monotonic language learning and their characterization
inferred← citeshttps://doi.org/10.48550/arxiv.1801.10502openalex-snapshot
Learning from Informants: Relations between Learning Success Criteria
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Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners
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