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128
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ICML
2010
IEEE
15 years 4 months ago
Active Learning for Networked Data
We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the label...
Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor
132
Voted
RECOMB
2005
Springer
16 years 3 months ago
Lower Bounds for Maximum Parsimony with Gene Order Data
Abstract. In this paper, we study lower bound techniques for branchand-bound algorithms for maximum parsimony, with a focus on gene order data. We give a simple O(n3 ) time dynamic...
Abraham Bachrach, Kevin Chen, Chris Harrelson, Rad...
154
Voted
IDEAS
2007
IEEE
148views Database» more  IDEAS 2007»
15 years 10 months ago
Adaptive Execution of Stream Window Joins in a Limited Memory Environment
A sliding window join (SWJoin) is becoming an integral operation in every stream data management system. In some streaming applications the increasing volume of streamed data as w...
Fatima Farag, Moustafa A. Hammad
146
Voted
GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
15 years 4 months ago
Informative sampling for large unbalanced data sets
Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is ...
Zhenyu Lu, Anand I. Rughani, Bruce I. Tranmer, Jos...
138
Voted
SIGMOD
1997
ACM
134views Database» more  SIGMOD 1997»
15 years 7 months ago
Scalable Parallel Data Mining for Association Rules
One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consu...
Eui-Hong Han, George Karypis, Vipin Kumar