Name
Abstract | ||
|---|---|---|
We present a series of algorithms with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boosting-style algorithm applicable in the context of structured prediction with a large number of labels. We also report the results of extensive experiments with these algorithms. |
Year | Venue | Field |
|---|---|---|
2014 | PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | Computer science,Structured prediction,Artificial intelligence,Machine learning,Randomized algorithms as zero-sum games |
DocType | Volume | Citations |
Conference | P14-1 | 1 |
PageRank | References | Authors |
0.37 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Corinna Cortes | 1 | 6574 | 1120.50 |
| Vitaly Kuznetsov | 2 | 68 | 9.33 |
| Mehryar Mohri | 3 | 4502 | 448.21 |