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COLING
2010
14 years 10 months ago
A Comparison of Models for Cost-Sensitive Active Learning
Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ann...
Katrin Tomanek, Udo Hahn
111
Voted
JMLR
2010
117views more  JMLR 2010»
14 years 10 months ago
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
Many real world applications employ multivariate performance measures and each example can belong to multiple classes. The currently most popular approaches train an SVM for each ...
Xinhua Zhang, Thore Graepel, Ralf Herbrich
CVPR
2012
IEEE
13 years 5 months ago
Learning to segment dense cell nuclei with shape prior
We study the problem of segmenting multiple cell nuclei from GFP or Hoechst stained microscope images with a shape prior. This problem is encountered ubiquitously in cell biology ...
Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt,...
224
Voted
TSE
2010
140views more  TSE 2010»
15 years 1 months ago
Learning a Metric for Code Readability
—In this paper, we explore the concept of code readability and investigate its relation to software quality. With data collected from 120 human annotators, we derive associations...
Raymond P. L. Buse, Westley Weimer
156
Voted
TSP
2010
14 years 10 months ago
Double sparsity: learning sparse dictionaries for sparse signal approximation
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
Ron Rubinstein, Michael Zibulevsky, Michael Elad