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ML
2007
ACM
156views Machine Learning» more  ML 2007»
14 years 9 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
CORR
2011
Springer
188views Education» more  CORR 2011»
14 years 4 months ago
Towards Unbiased BFS Sampling
Abstract—Breadth First Search (BFS) is a widely used approach for sampling large unknown Internet topologies. Its main advantage over random walks and other exploration technique...
Maciej Kurant, Athina Markopoulou, Patrick Thiran
PRIB
2010
Springer
242views Bioinformatics» more  PRIB 2010»
14 years 8 months ago
Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis
Abstract. Supervised classifiers require manually labeled training samples to classify unlabeled objects. Active Learning (AL) can be used to selectively label only “ambiguous...
Scott Doyle, Anant Madabhushi
SODA
2010
ACM
209views Algorithms» more  SODA 2010»
15 years 7 months ago
Counting Stars and Other Small Subgraphs in Sublinear Time
Detecting and counting the number of copies of certain subgraphs (also known as network motifs or graphlets), is motivated by applications in a variety of areas ranging from Biolo...
Mira Gonen, Dana Ron, Yuval Shavitt
GLOBECOM
2008
IEEE
15 years 4 months ago
Impact of Mobility on the Behavior of Interference in Cellular Wireless Networks
In this study, the impact of mobility is investigated in low-speed environments such as femtocells and picocells for wireless networks. Given that there is interference on the upl...
Serhan Yarkan, Amine Maaref, Koon Hoo Teo, Hü...