On the sample complexity of learning functions with bounded variation
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning fixed-dimension linear thresholds from fragmented data
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Learning fixed-dimension linear thresholds from fragmented data
Information and Computation
Geometric Methods in the Analysis of Glivenko-Cantelli Classes
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Efficient algorithms for learning functions with bounded variation
Information and Computation
Maximal width learning of binary functions
Theoretical Computer Science
Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks
International Journal of Applied Mathematics and Computer Science
Multi-instance learning with any hypothesis class
The Journal of Machine Learning Research
| Hi-index | 754.84 |
We find tight upper and lower bounds on the growth rate for the covering numbers of functions of bounded variation in the L1 metric in terms of all the relevant constants. We also find upper and lower bounds on covering numbers for general function classes over the family of L1(dP) metrics in terms of a scale-sensitive combinatorial dimension of the function class