Coding and information theory (2nd ed.)
Coding and information theory (2nd ed.)
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The correct definition of finite elasticity: corrigendum to identification of unions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
A noise model on learning sets of strings
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Types of monotonic language learning and their characterization
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A machine discovery from amino acid sequences by decision trees over regular patterns
Selected papers of international conference on Fifth generation computer systems 92
Towards a mathematical theory of machine discovery from facts
Theoretical Computer Science - Special issue on algorithmic learning theory
The String-to-String Correction Problem
Journal of the ACM (JACM)
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Properties of Language Classes With Finite Elasticity
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Learning Theory Toward Genome Informatics
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Program Synthesis in the Presence of Infinite Number of Inaccuracies
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Machine Discovery in the Presence of Incomplete or Ambiguous Data
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Inductive Inference of an Approximate Concept from Positive Data
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
On Approximately Identifying Concept Classes in the Limit
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Synthesizing Learners Tolerating Computable Noisy Data
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Characteristic Sets for Unions of Regular Pattern Languages and Compactness
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Inferring a Rewriting System from Examples
DS '98 Proceedings of the First International Conference on Discovery Science
Language Learning with a Neighbor System
DS '00 Proceedings of the Third International Conference on Discovery Science
Refutable language learning with a neighbor system
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Finding patterns common to a set of strings (Extended Abstract)
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
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The paper develops the theory of refutable/inductive learning as a foundation of discovery science from examples. We consider refutable/inductive language learning from positive examples, some of which may be incorrect. The error or incorrectness we consider is the one described uniformly in terms of a distance over strings. We define a k-neighbor closure of a language L as the collection of strings each of which is at most k distant from some string in L. In ordinary learning paradigm, a target language is assumed to belong to a hypothesis space without any guarantee. In this paper, we allow an inference machine to infer a neighbor closure instead of the original language as an admissible approximation. We formalize such kind of learning, and give some sufficient conditions for a hypothesis space.As its application to concrete problems, we deal with languages defined by decision trees over patterns. The problem of learning decision trees over patterns has been studied from a viewpoint of knowledge discovery for Genome information processing in the framework of PAC learning from both positive and negative examples. We investigate their learnability in the limit from neighbor examples as well as refutable learnability from complete examples, i.e., from both positive and negative examples. Furthermore, we present some procedures which plays an important role for designing efficient learning algorithms for decision trees over regular patterns.