STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning Nested Differences of Intersection-Closed Concept Classes
Machine Learning
Teachability in computational learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
A computational model of teaching
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
Learning binary relations and total orders
SIAM Journal on Computing
The Power of Self-Directed Learning
Machine Learning
Journal of Computer and System Sciences
Discrete Applied Mathematics - Special issue: Vapnik-Chervonenkis dimension
Machine Learning
Machine Learning
On Teaching and Learning Intersection-Closed Concept Classes
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Unlabeled Compression Schemes for Maximum Classes
The Journal of Machine Learning Research
Measuring teachability using variants of the teaching dimension
Theoretical Computer Science
Models of Cooperative Teaching and Learning
The Journal of Machine Learning Research
Sauer's bound for a notion of teaching complexity
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Hi-index | 0.00 |
This paper is concerned with the combinatorial structure of concept classes that can be learned from a small number of examples. We show that the recently introduced notion of recursive teaching dimension (RTD, reflecting the complexity of teaching a concept class) is a relevant parameter in this context. Comparing the RTD to self-directed learning, we establish new lower bounds on the query complexity for a variety of query learning models and thus connect teaching to query learning. For many general cases, the RTD is upper-bounded by the VC-dimension, e.g., classes of VC-dimension 1, (nested differences of) intersection-closed classes, "standard" boolean function classes, and finite maximum classes. The RTD thus is the first model to connect teaching to the VC-dimension. The combinatorial structure defined by the RTD has a remarkable resemblance to the structure exploited by sample compression schemes and hence connects teaching to sample compression. Sequences of teaching sets defining the RTD coincide with unlabeled compression schemes both (i) resulting from Rubinstein and Rubinstein's corner-peeling and (ii) resulting from Kuzmin and Warmuth's Tail Matching algorithm.