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» On a theory of learning with similarity functions
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CVPR
2009
IEEE
16 years 7 months ago
Active Learning for Large Multi-class Problems
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods ...
Prateek Jain (University of Texas at Austin), Ashi...
ACMSE
2010
ACM
14 years 7 months ago
Learning to rank using 1-norm regularization and convex hull reduction
The ranking problem appears in many areas of study such as customer rating, social science, economics, and information retrieval. Ranking can be formulated as a classification pro...
Xiaofei Nan, Yixin Chen, Xin Dang, Dawn Wilkins
CIKM
2000
Springer
15 years 4 months ago
Boosting for Document Routing
RankBoost is a recently proposed algorithm for learning ranking functions. It is simple to implement and has strong justifications from computational learning theory. We describe...
Raj D. Iyer, David D. Lewis, Robert E. Schapire, Y...
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 8 days ago
Mining for proposal reviewers: lessons learned at the national science foundation
In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The appl...
Seth Hettich, Michael J. Pazzani
EMO
2009
Springer
159views Optimization» more  EMO 2009»
15 years 6 months ago
Recombination for Learning Strategy Parameters in the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
Thomas Voß, Nikolaus Hansen, Christian Igel