Learning decision trees from random examples needed for learning
Information and Computation
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning Boolean Functions in an Infinite Attribute Space
Machine Learning
Inductive logic programming and learnability
ACM SIGART Bulletin
General bounds on the number of examples needed for learning probabilistic concepts
Journal of Computer and System Sciences
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Generalization in decision trees and DNF: does size matter?
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
The Haskell: The Craft of Functional Programming
The Haskell: The Craft of Functional Programming
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Classification of Individuals with Complex Structure
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Acyclic First-Order Horn Sentences from Entailment
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Towards tight bounds for rule learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Pac-learning recursive logic programs: efficient algorithms
Journal of Artificial Intelligence Research
What should be minimized in a decision tree?
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
(Agnostic) PAC learning concepts in higher-order logic
ECML'06 Proceedings of the 17th European conference on Machine Learning
Hi-index | 0.00 |
This paper is concerned with generalization issues for a decision tree learner for structured data called Alkemy. Motivated by error bounds established in statistical learning theory, we study the VC dimensions of some predicate classes defined on sets and multisets – two data-modelling constructs used intensively in the knowledge representation formalism of Alkemy – and from that obtain insights into the (worst-case) generalization behaviour of the learner. The VC dimension results and the techniques used to derive them may be of wider independent interest.