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KDD
2012
ACM
178views Data Mining» more  KDD 2012»
12 years 2 months ago
Mining emerging patterns by streaming feature selection
Building an accurate emerging pattern classifier with a highdimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unava...
Kui Yu, Wei Ding 0003, Dan A. Simovici, Xindong Wu
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
12 years 2 months ago
Unsupervised feature selection for linked social media data
The prevalent use of social media produces mountains of unlabeled, high-dimensional data. Feature selection has been shown eļ¬€ective in dealing with high-dimensional data for eļ¬...
Jiliang Tang, Huan Liu
CLEF
2010
Springer
14 years 1 months ago
Web Person Name Disambiguation by Relevance Weighting of Extended Feature Sets
Abstract. This paper describes our approach to the Person Name Disambiguation clustering task in the Third Web People Search Evaluation Campaign(WePS3). The method focuses on two a...
Chong Long, Lei Shi
CISST
2004
164views Hardware» more  CISST 2004»
14 years 1 months ago
Probabilistic Region Relevance Learning for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Iker Gondra, Douglas R. Heisterkamp
DAGM
2009
Springer
14 years 7 months ago
Learning with Few Examples by Transferring Feature Relevance
The human ability to learn diļ¬ƒcult object categories from just a few views is often explained by an extensive use of knowledge from related classes. In this work we study the use...
Erik Rodner, Joachim Denzler
ICML
2007
IEEE
15 years 1 months ago
Learning a meta-level prior for feature relevance from multiple related tasks
In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a p...
Su-In Lee, Vassil Chatalbashev, David Vickrey, Dap...