
אריה קנטורוביץ
אקדמי בכיר
Near-Optimal Sample Compression for Nearest Neighbors
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 performance 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.
| שפת פרסום | אנגלית |
| דפים | 4120-4128 |
| כתב עת | IEEE Transactions on Information Theory |
| כרך | 64 |
| נושא מספר | 6 |
| סטטוס פרסום | פורסם - 01.06.2018 |
Keywords
Nearest neighbor methods
ASJC Scopus subject areas
Information Systems
Computer Science Applications
Library and Information Sciences