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» Approximation Methods for Supervised Learning
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ICDM
2009
IEEE
165views Data Mining» more  ICDM 2009»
14 years 4 months ago
Cross-Guided Clustering: Transfer of Relevant Supervision across Domains for Improved Clustering
—Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically tr...
Indrajit Bhattacharya, Shantanu Godbole, Sachindra...
ICML
2008
IEEE
14 years 10 months ago
Democratic approximation of lexicographic preference models
Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democra...
Fusun Yaman, Thomas J. Walsh, Michael L. Littman, ...
ECCV
2008
Springer
14 years 12 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...
ACL
2010
13 years 8 months ago
Learning Better Data Representation Using Inference-Driven Metric Learning
We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by ...
Paramveer S. Dhillon, Partha Pratim Talukdar, Koby...
PRIB
2010
Springer
242views Bioinformatics» more  PRIB 2010»
13 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