A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
In many practical domains, misclassification costs can differ greatly and may be represented by class ratios, however, most learning algorithms struggle with skewed class distrib...
William Klement, Peter A. Flach, Nathalie Japkowic...
Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dualobjective scheduling problems, where the operating...
In recent years, there has been significant interest in development of ranking functions and efficient top-k retrieval algorithms to help users in ad-hoc search and retrieval in da...
Muhammed Miah, Gautam Das, Vagelis Hristidis, Heik...
In an online linear optimization problem, on each period t, an online algorithm chooses st S from a fixed (possibly infinite) set S of feasible decisions. Nature (who may be adve...