Information Processing Letters
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Lower Bound Methods and Separation Results for On-Line Learning Models
Machine Learning - Computational learning theory
Inductive logic programming and learnability
ACM SIGART Bulletin
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
How many queries are needed to learn?
Journal of the ACM (JACM)
Logical settings for concept-learning
Artificial Intelligence
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning logic programs with structured background knowledge
Artificial Intelligence
Learning closed horn expressions
Information and Computation
Machine Learning
Machine Learning
Learnability and Definability in Trees and Similar Structures
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
Learning Acyclic First-Order Horn Sentences from Entailment
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Learning Horn Definitions with Equivalence and Membership Queries
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning First-Order Acyclic Horn Programs from Entailment
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Pac-learning recursive logic programs: efficient algorithms
Journal of Artificial Intelligence Research
Artificial Intelligence in modelling the complexity of Mediterranean landscape transformations
Computers and Electronics in Agriculture
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We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of parameters that are used in the literature and thus they cannot give a complete characterization in terms of learnability with polynomial resources. We then identify an alternative notion of size and a simple set of parameters that are useful for first order Horn Expressions. These parameters are the number of clauses in the expression, the maximum number of distinct terms in a clause, and the maximum number of literals in a clause. Matching lower bounds derived using the Vapnik Chervonenkis dimension complete the picture showing that these parameters are indeed crucial.