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» Learning Subjective Functions with Large Margins
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2009
ACM
14 years 3 months ago
Lessons learned from a year's worth of benchmarks of large data clouds
In this paper, we discuss some of the lessons that we have learned working with the Hadoop and Sector/Sphere systems. Both of these systems are cloud-based systems designed to sup...
Yunhong Gu, Robert L. Grossman
ICML
2007
IEEE
14 years 9 months ago
Asymmetric boosting
A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic princip...
Hamed Masnadi-Shirazi, Nuno Vasconcelos
JMLR
2010
135views more  JMLR 2010»
13 years 3 months ago
Structured Prediction Cascades
Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference i...
David Weiss, Benjamin Taskar
SIGIR
2004
ACM
14 years 1 months ago
Learning effective ranking functions for newsgroup search
Web communities are web virtual broadcasting spaces where people can freely discuss anything. While such communities function as discussion boards, they have even greater value as...
Wensi Xi, Jesper Lind, Eric Brill
WWW
2011
ACM
13 years 3 months ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...