Name
Abstract | ||
|---|---|---|
In this paper, we describe a fixed-threshold sequential minimal optimization (FSMO) for a joint constraint learning algorithm of structural classification SVM problems. Because FSMO uses the fact that the joint constraint formulation of structural SVM has b=0, FSMO breaks down the quadratic programming (QP) problems of structural SVM into a series of smallest QP problems, each involving only one variable. By using only one variable, FSMO is advantageous in that each QP sub-problem does not need subset selection. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1390334.1390526 | SIGIR |
Keywords | Field | DocType |
structural classification svm problem,structural svm,joint constraint,fixed-threshold smo,joint constraint formulation,quadratic programming,subset selection,fixed-threshold sequential minimal optimization,smallest qp problem,joint constraint learning algorithm,qp sub-problem,quadratic program,sequential minimal optimization | Pattern recognition,Computer science,Structural classification,Support vector machine,Algorithm,Artificial intelligence,Constraint learning,Quadratic programming,Sequential minimal optimization | Conference |
Citations | PageRank | References |
1 | 0.38 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changki Lee | 1 | 279 | 26.18 |
| Hyun-Ki Kim | 2 | 61 | 21.35 |
| Myung-gil Jang | 3 | 173 | 17.43 |