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Conference Paper

Maximum Margin Semi-Supervised Learning for Structured Variables

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Altun, Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Altun, Y., McAllester, D., & Belkin, M. (2006). Maximum Margin Semi-Supervised Learning for Structured Variables. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, 33-40.

Abstract

Many real-world classification problems involve the prediction of multiple inter-dependent variables forming some structural dependency. Recent progress in machine learning has mainly focused on supervised classification of such structured variables. In this paper, we investigate structured classification in a semi-supervised setting. We present a discriminative approach that utilizes the intrinsic geometry of input patterns revealed by unlabeled data points and we derive a maximum-margin formulation of semi-supervised learning for structured variables. Unlike transductive algorithms, our formulation naturally extends to new test points.