We present a new active learning approach to incorporate
human feedback for on-line unusual event detection. In contrast to most
existing unsupervised methods that perform passiv...
Multi agent learning systems pose an interesting set of problems: in large environments agents may develop localised behaviour patterns that are not necessarily optimal; in a pure...
In this paper, an anomaly detection approach that fuses data gathered from different nodes in a distributed wireless sensor network is proposed and evaluated. The emphasis of this...
Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recom...
Sheng Zhang, Amit Chakrabarti, James Ford, Fillia ...