Online and co-regularized algorithms for large scale learning

In this work I address the issue of large scale learning in an online setting. To tackle it, I introduce a novel algorithm that enables semi-supervised learning in an online fashion. By combining state-of-the-art online methods such as Pegasos [3] with the multi-view co-regularization framework, I achieve significantly better performance on regression and binary classification tasks. This shows that incorporation of unlabeled data is still practical even in large scale and online settings. Evaluation is done on several publicly available datasets from the UCI and LibSVM repositories. To evaluate results in a practical setting, I also consider a difficult natural language set from the BioInfer corpus [18]. In this setting the introduced algorithm outperforms current state-of-the-art methods for online learning. The main contents of this work will also be presented at the conference Discovery Science 2012 and contained in Lectures of Computer Science [21].