Name
Abstract | ||
|---|---|---|
Label sequence learning is the problem of inferring a state se- quence from an observation sequence, where the state sequence may encode a labeling, annotation or segmentation of the se- quence. In this paper we give an overview of discriminative methods developed for this problem. Special emphasis is put on large margin methods by generalizing multiclass Support Vec- tor Machines and AdaBoost to the case of label sequences. An experimental evaluation demonstrates the advantages over clas- sical approaches like Hidden Markov Models and the competi- tiveness with methods like Conditional Random Fields. |
Year | Venue | Keywords |
|---|---|---|
2003 | INTERSPEECH | sequence learning,hidden markov model,conditional random field |
Field | DocType | Citations |
Conditional random field,AdaBoost,Sequence labeling,Maximum-entropy Markov model,Pattern recognition,Computer science,Variable-order Markov model,Artificial intelligence,Hidden Markov model,Discriminative model,Sequence learning,Machine learning | Conference | 11 |
PageRank | References | Authors |
1.60 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yasemin altun | 1 | 2463 | 150.46 |
| Thomas Hofmann | 2 | 10064 | 1001.83 |