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PKDD
2010
Springer
164views Data Mining» more  PKDD 2010»
13 years 7 months ago
Complexity Bounds for Batch Active Learning in Classification
Active learning [1] is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their l...
Philippe Rolet, Olivier Teytaud
PAKDD
2011
ACM
473views Data Mining» more  PAKDD 2011»
13 years 3 months ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is pr...
Timothy Hospedales, Shaogang Gong and Tao Xiang
JUCS
2008
125views more  JUCS 2008»
13 years 9 months ago
Improving AEH Courses through Log Analysis
: Authoring in adaptive educational hypermedia environment is complex activity. In order to promote a wider application of this technology, the teachers and course designers need s...
César Vialardi Sacín, Javier Bravo, ...
ICDM
2008
IEEE
172views Data Mining» more  ICDM 2008»
14 years 4 months ago
Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference
Selecting promising queries is the key to effective active learning. In this paper, we investigate selection techniques for the task of learning an equivalence relation where the ...
Steffen Rendle, Lars Schmidt-Thieme
KDD
2012
ACM
281views Data Mining» more  KDD 2012»
12 years 5 days ago
Active spectral clustering via iterative uncertainty reduction
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforw...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord...