In this paper, we propose a semi-supervised learning approach for classifying program (bot) generated web search traffic from that of genuine human users. The work is motivated by...
Hongwen Kang, Kuansan Wang, David Soukal, Fritz Be...
Queries describe the users' search intent and therefore they play an essential role in the context of ranking for information retrieval and Web search. However, most of exist...
In this paper we describe the first stage of a new learning system for object detection and recognition. For our system we propose Boosting [5] as the underlying learning technique...
Andreas Opelt, Michael Fussenegger, Axel Pinz, Pet...
RADAR is a multiagent system with a mixed-initiative user interface designed to help office workers cope with email overload. RADAR agents observe experts to learn models of their...
Aaron Steinfeld, Andrew Faulring, Asim Smailagic, ...
In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-ti...