Research on Semi-Supervised Support Vector Machine

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• Dr. Yu-Feng Li, LAMDA Group, National Key Laboratory for Novel Software Technology, Nanjing University
• Wednesday 20 August 2014, 16:00-17:00
• UG05, Learning Centre.

If you have a question about this talk, please contact Leandro Minku.

Host: Dr. Shan He

Exploiting unlabelled data to help improve the performance is fundamental to machine learning research. Semi-Supervised learning is one major branch in this direction, and Semi-Supervised SVM (S3VM) is one of the most basic, popular Semi-Supervised learning paradigms. Many researchers have studied S3VM since S3VM was proposed. However, the scalability, the efficiency, the safeness, and the learning cost of S3V Ms have not been well addressed. In this paper, I will present some of our recent progress on these issues.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• UG05, Learning Centre
• computer sience

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