The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective ...
Shenzhi Li, Christopher D. Janneck, Aditya P. Bela...
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
The increasingly popular GPS technology and the growing amount of trajectory data it generates create the need for developing applications that efficiently store and query traject...
Petko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotra...
Higher Education Institutions are now spending a significant portion of their budget to implement and maintain modern Enterprise Resource Planning (ERP) solutions. ERP is a softwa...
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...