Learning from good and bad data
Learning from good and bad data
Foundations of deductive databases and logic programming
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
Inference of Reversible Languages
Journal of the ACM (JACM)
Inductive inference of unbounded unions of pattern languages from positive data
Theoretical Computer Science - Special issue on algorithmic learning theory
On learning unions of pattern languages and tree patterns in the mistake bound model
Theoretical Computer Science
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Learning Unions of Tree Patterns Using Queries
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
A Polynomial Time Algorithm for Finding Finite Unions of Tree Pattern Languages
Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logic
| Hi-index | 0.00 |
In the present paper we clarify the combination of the MINL (MINimal Langugae) strategy and refinement operators in the model of identification in the limit from positive data, by giving a learning procedure in a general form adopting both of the two. The MINL strategy is to choose minimal concepts consistent with given examples as guesses, and has been adopted in many previous works in the model. The minimality of concepts is defined w.r.t. the set-inclusion relation, and so the strategy is semantic-based. Refinement operators have developed in the field of learning logic programs to construct logic programs as hypotheses consistent with logical formulae given as examples. The operators are defined based on inference rules in first-order logic and so are syntactical. With the proposed procedure we give such a new class of tree pattern languages that every finite unions of the languages is identifiable from positive data without assuming the upperbound of the number of unions. Moreover, we revise the algorithm so that we can show that the class is polynomial time identifiable.