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2006 JMLR JMLR 2006

Large Scale Transductive SVMs

Abstract

We show how the concave-convex procedure can be applied to transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach. Software is available at http://www.kyb.tuebingen.mpg.de/bs/people/fabee/transduction.html. [abs] [ pdf ][ bib ] © JMLR 2006. (edit, beta)

🧭 Keyword Pioneer — concave-convex procedure
🐣 Hot Topic Early Bird — semi-supervised learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Ronan Collobert , Fabian Sinz , Jason Weston , Léon Bottou

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Core Methods > Support Vector Machine

Keywords

semi-supervised learning transductive learning concave-convex procedure support vector machine scalable algorithm
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