Latent structural SVM with marginal probabilities for weakly labeled structured learning
Shahin Rahmatollahi Namin, José Manuel Álvarez, Laurent Kneip, Lars Petersson · 2016
In the last years, the increasing availability of annotated data has facilitated the great success of supervised learning in real-world applications such as semantic labeling. However, the vast majority of data is nowadays unlabeled or partially annotated. In this paper, we develop an Expected Marginal Latent Structural SVM (EM-LSSVM) framework for performing structured learning in the presence of weakly (partially) annotated data by incorporating the uncertainty of the unobserved data as marginals. Experimental results on semantic labeling show the potential of the proposed method. In particular, we learn the parameters of a CRF where large amounts of noisy and unobserved data are available. Comparison against state of the art demonstrates the applicability of our algorithm to practical applications.