상세정보
학술대회
Fixed-threshold SMO for Joint Constraint Learning Algorithm of Structural SVM
- 발행일
- 200807
- 출처
- International Conference on Research and Development in Information Retrieval 2008, pp.829-830
- 협약과제
- 08MS3700, 웹 QA 기술개발, 장명길
- 초록
- 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, FSMD 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.
- KSP 제안 키워드
- Joint constraint, Structural SVM(SSVM), Subset selection, breaks down, constraint learning, learning algorithms, quadratic programming, sequential minimal optimization