Learning with refutation
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TLDR
This study considers general classes of recursively enumerable languages and allows the machine to either identify or refute the unrepresentative texts (respectively, texts containing finite un representative samples).About:
This article is published in Journal of Computer and System Sciences.The article was published on 1998-12-01 and is currently open access. It has received 11 citations till now. The article focuses on the topics: Recursively enumerable language.read more
Citations
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Journal ArticleDOI
Refuting learning revisited
Wolfgang Merkle,Frank Stephan +1 more
TL;DR: The concept of refuting learning as introduced by Mukouchi and Arikawa is considered, where the learner is not only required to learn all concepts in a given class but also has to explicitly refute concepts outside the class.
Journal ArticleDOI
Reflective inductive inference of recursive functions
TL;DR: This paper compares the learning power of reflective IIMs with each other as well as with the one of standard IIMs for learning in the limit, for consistent learning of three different types, and for finite learning.
Book ChapterDOI
Reflective Inductive Inference of Recursive Functions
TL;DR: In this paper, the authors investigate reflective inductive inference of recursive functions and compare the learning power of reflective IIMs with each other as well as with the one of standard IIMs.
Book ChapterDOI
Language Learning with a Neighbor System
Yasuhito Mukouchi,Masako Sato +1 more
TL;DR: This paper allows an inference machine to infer a neighbor closure instead of the original language as an admissible approximation, and formalizes such kind of inference, and gives some sufficient conditions for a hypothesis space.
Book ChapterDOI
Refutable Language Learning with a Neighbor System
Yasuhito Mukouchi,Masako Sato +1 more
TL;DR: A notion of a recursively generable distance over strings is introduced, and formal definitions of k-neighbor (refutable) inferability are given, and necessary and sufficient conditions on such kinds of inference are discussed.
References
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Journal ArticleDOI
Theory of Recursive Functions and Effective Computability.
Solomon Feferman,Hartley Rogers +1 more
TL;DR: Central concerns of the book are related theories of recursively enumerable sets, of degree of un-solvability and turing degrees in particular and generalizations of recursion theory.
Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
Journal ArticleDOI
Language identification in the limit
TL;DR: It was found that theclass of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learningable from a text.
Journal ArticleDOI
A Machine-Independent Theory of the Complexity of Recursive Functions
TL;DR: The number of steps required to compute a function depends on the type of computer that is used, on the choice of computer program, and on the input-output code, but the results obtained in this paper are nearly independent of these considerations.
Book ChapterDOI
Machine Inductive Inference and Language Identification
John Case,Christopher Lynes +1 more
TL;DR: It is shown that for some classes of recursive languages, from the characteristic function of any L in ℒ an approximate decision procedure for L with no more than n+1 mistakes can be (uniformly effectively) inferred in the limit; whereas, in general, a grammar with no less than n mistakes cannot be inferred.