Maximum Margin Semi-Supervised Learning for Structured Variables
Yasemin Altün, David McAllester, Mikhail Belkin · 2005
Abstract Many real-world classification problems involve the prediction ofmultiple inter-dependent variables forming some structural dependency. Recent progress in machine learning has mainly focused onsupervised 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 wederive a maximum-margin formulation of semi-supervised learning for structured variables. Unlike transductive algorithms, our for-mulation naturally extends to new test points.