Abstract
We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds. Some encouraging empirical results are also presented.
| Original language | English |
|---|---|
| Pages (from-to) | 370-378 |
| Number of pages | 9 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 1 |
| Issue number | January |
| State | Published - 2014 |
| Event | 28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada Duration: 8 Dec 2014 → 13 Dec 2014 |