Epsilon-nets and simplex range queries
SCG '86 Proceedings of the second annual symposium on Computational geometry
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Concept learning with geometric hypotheses
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The VC dimension of k-fold union
Information Processing Letters
Exploiting label dependencies for improved sample complexity
Machine Learning
| Hi-index | 0.89 |
We show that 2 is the minimum VC dimension of a concept class whose k-fold union has VC dimension @W(klogk).