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Chernoff-Hoeffding Inequality
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lemma, Johnson-Lindenstrauss |
| Copyright Year | 2017 |
| Abstract | When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making “many simple estimates” of the full data set, and then judging them as a whole. Perhaps magically, these “many simple estimates” can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality [2, 5]. This document provides a simple form of this bound, and two examples of its use. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.utah.edu/~jeffp/teaching/cs5140/L2+Chern-Hoeff.pdf |
| Alternate Webpage(s) | http://www2.cs.utah.edu/~jeffp/teaching/cs5140/L2+Chern-Hoeff.pdf |
| Alternate Webpage(s) | http://www2.cs.utah.edu/~jeffp/DMBook/L2+Chern-Hoeff.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |