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» A supervised learning approach for imbalanced data sets
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KDD
2006
ACM
156views Data Mining» more  KDD 2006»
16 years 6 months ago
Unsupervised learning on k-partite graphs
Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the resear...
Bo Long, Xiaoyun Wu, Zhongfei (Mark) Zhang, Philip...
IROS
2009
IEEE
198views Robotics» more  IROS 2009»
16 years 1 months ago
Scalable learning for object detection with GPU hardware
Abstract— We consider the problem of robotic object detection of such objects as mugs, cups, and staplers in indoor environments. While object detection has made significant pro...
Adam Coates, Paul Baumstarck, Quoc V. Le, Andrew Y...
PR
2006
141views more  PR 2006»
15 years 6 months ago
Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval
The performance of many supervised and unsupervised learning algorithms is very sensitive to the choice of an appropriate distance metric. Previous work in metric learning and ada...
Hong Chang, Dit-Yan Yeung, William K. Cheung
ICML
2007
IEEE
16 years 7 months ago
Graph clustering with network structure indices
Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the k-medoids algorithm and the Girvan...
Matthew J. Rattigan, Marc Maier, David Jensen
ICDM
2010
IEEE
127views Data Mining» more  ICDM 2010»
15 years 4 months ago
Learning Markov Network Structure with Decision Trees
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
Daniel Lowd, Jesse Davis