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» An Empirical Evaluation of Supervised Learning for ROC Area
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GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
15 years 1 months ago
Improving GP classifier generalization using a cluster separation metric
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Ashley George, Malcolm I. Heywood
ECML
2007
Springer
15 years 3 months ago
A Simple Lexicographic Ranker and Probability Estimator
Given a binary classification task, a ranker sorts a set of instances from highest to lowest expectation that the instance is positive. We propose a lexicographic ranker, LexRank,...
Peter A. Flach, Edson Takashi Matsubara
IJCNN
2006
IEEE
15 years 3 months ago
Learning to Rank by Maximizing AUC with Linear Programming
— Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. Several researchers have approached AUC optimization by approxi...
Kaan Ataman, W. Nick Street, Yi Zhang
BMCBI
2008
116views more  BMCBI 2008»
14 years 9 months ago
MScanner: a classifier for retrieving Medline citations
Background: Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of t...
Graham L. Poulter, Daniel L. Rubin, Russ B. Altman...
CVPR
2011
IEEE
14 years 4 months ago
Supervised Hierarchical Pitman-Yor Process for Natural Scene Segmentation
From conventional wisdom and empirical studies of annotated data, it has been shown that visual statistics such as object frequencies and segment sizes follow power law distributi...
Alex Shyr, Trevor Darrell, Michael Jordan, Raquel ...