For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, c...
Organizations have become increasingly dependent on computing systems to achieve their business goals. The performance of these systems in terms of response times and cost has a m...
M. Qin, R. Lee, Asham El Rayess, Vidar Vetland, Je...
The problem of choosing a good parameter setting for a better generalization performance in a learning task is the so-called model selection. A nested uniform design (UD) methodol...
Chien-Ming Huang, Yuh-Jye Lee, Dennis K. J. Lin, S...
Traditional performance models are too brittle to be relied on for continuous capacity planning and performance debugging in many computer systems. Simply put, a brittle model is ...
Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural ...