Name
Abstract | ||
|---|---|---|
We consider smoothed versions of geometric range spaces, so an element of the ground set e.g. a point can be contained in a range with a non-binary value in [0,﾿1]. Similar notions have been considered for kernels; we extend them to more general types of ranges. We then consider approximations of these range spaces through $$\\varepsilon $$-nets and $$\\varepsilon $$-samples aka $$\\varepsilon $$-approximations. We characterize when size bounds for $$\\varepsilon $$-samples on kernels can be extended to these more general smoothed range spaces. We also describe new generalizations for $$\\varepsilon $$-nets to these range spaces and show when results from binary range spaces can carry over to these smoothed ones. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24486-0_15 | International Conference on Algorithmic Learning Theory |
Field | DocType | Volume |
Discrete mathematics,Generalization,Approximations of π,Mathematics,AKA,Binary number | Conference | abs/1510.09123 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeff M. Phillips | 1 | 536 | 49.83 |
| Yan Zheng | 2 | 24 | 2.98 |