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Optimization Techniques for Semi-Supervised Support Vector Machines




Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S3VMs. This paper reviews key ideas in this literature. The performance and behavior of various S3VMs algorithms is studied together, under a common experimental setting.

Author(s): Chapelle, O. and Sindhwani, V. and Keerthi, SS.
Journal: Journal of Machine Learning Research
Volume: 9
Pages: 203-233
Year: 2008
Month: February
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Optimization Techniques for Semi-Supervised Support Vector Machines},
  author = {Chapelle, O. and Sindhwani, V. and Keerthi, SS.},
  journal = {Journal of Machine Learning Research},
  volume = {9},
  pages = {203-233},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2008},
  month_numeric = {2}