Online Cost Sensitive Domain Knowledge Adaption

Online Active Learning for Cost Sensitive Domain Adaptation

Active learning and domain adaptation are both important tools for reducing labeling effort to learn a good supervised model in a target domain. In this paper, we investigate the problem of online active learning within a new active domain adaptation setting: there are insufficient labeled data in both source and target domains, but it is cheaper to query labels in the source domain than in the...

Cost Sensitive Online Multiple Kernel Classification

Learning from data streams has been an important open research problem in the era of big data analytics. This paper investigates supervised machine learning techniques for mining data streams with application to online anomaly detection. Unlike conventional machine learning tasks, machine learning from data streams for online anomaly detection has several challenges: (i) data arriving sequentia...

Contextsensitive Online Adaption of Workflows

A dynamic and complex process environment forces the automated execution and monitoring of processes to face a lot of hard problems. This paper shows how the execution of agile workflows can be assisted by AI planning techniques. In contrast to previous approaches, this method allows for an online adaption of simple process models to changes in the process environment. It can handle a manifold ...

Supervised Domain Adaption for WSD

The lack of positive results on supervised domain adaptation for WSD have cast some doubts on the utility of handtagging general corpora and thus developing generic supervised WSD systems. In this paper we show for the first time that our WSD system trained on a general source corpus (BNC) and the target corpus, obtains up to 22% error reduction when compared to a system trained on the target c...

Online Policy Adaption for Ensemble Algorithms