Branch and Bound for Semi-Supervised Support Vector Machines

Chapelle, Olivier, Sindhwani, Vikas, Keerthi, S. S.

Dec-31-2007–Neural Information Processing Systems

VM) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimizationproblem is non-convex.

artificial intelligence, machine learning, objective function, (15 more...)

Neural Information Processing Systems

Dec-31-2007

Conferences PDF

Country:
- Europe > Germany
  - Baden-Württemberg > Tübingen Region > Tübingen (0.14)
- North America > United States (0.28)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning > Statistical Learning
    - Support Vector Machines (1.00)
  - Representation & Reasoning > Search (1.00)

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Branch and Bound for Semi-Supervised Support Vector Machines
Branch and Bound for Semi-Supervised Support Vector Machines

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