Name
Abstract | ||
|---|---|---|
We present a series of learning algorithms and theoretical guarantees for designing accurate ensembles of structured prediction tasks. This includes several 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 give a detailed study of all these algorithms, including the description of new on-line-to-batch conversions and learning guarantees. We also report the results of extensive experiments with these algorithms in several structured prediction tasks. |
Year | Venue | Field |
|---|---|---|
2014 | ICML | Computer science,Structured prediction,Theoretical computer science,Artificial intelligence,Ensemble learning,Machine learning,Randomized algorithms as zero-sum games |
DocType | Citations | PageRank |
Conference | 9 | 0.57 |
References | Authors | |
29 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Corinna Cortes | 1 | 6574 | 1120.50 |
| Vitaly Kuznetsov | 2 | 68 | 9.33 |
| Mehryar Mohri | 3 | 4502 | 448.21 |