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AAAI
2008
13 years 10 months ago
Active Learning for Pipeline Models
For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, c...
Dan Roth, Kevin Small
CASCON
1996
160views Education» more  CASCON 1996»
13 years 9 months ago
Automatic generation of performance models for distributed application systems
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...
CSDA
2007
105views more  CSDA 2007»
13 years 7 months ago
Model selection for support vector machines via uniform design
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...
SIGMETRICS
2008
ACM
13 years 7 months ago
Ironmodel: robust performance models in the wild
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 ...
Eno Thereska, Gregory R. Ganger
ML
2007
ACM
156views Machine Learning» more  ML 2007»
13 years 7 months ago
Active learning for logistic regression: an evaluation
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 ...
Andrew I. Schein, Lyle H. Ungar