| Creator |
|
|
|
|
|
| Language |
|
| Publisher |
|
| Date |
|
| Conference |
|
| Publication Type |
|
| Access Rights |
|
| Related DOI |
|
| Related DOI |
|
| Relation |
|
| Abstract |
We propose a new approach to large-scale machine learning, learning over compressed data: First compress the training data somehow and then employ various machine learning algorithms on the compressed... data, with the hope that the computation time is significantly reduced when the training data is well compressed. As the first step, we consider a variant of the Zero-Suppressed Binary Decision Diagram (ZDD) as the data structure for representing the training data, which is a generalization of the ZDD by incorporating non-determinism. For the learning algorithm to be employed, we consider boosting algorithm called AdaBoost∗ and its precursor AdaBoost. In this work, we give efficient implementations of the boosting algorithms whose running times (per iteration) are linear in the size of the given ZDD.show more
|
Export Mendeley
main