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
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¬...
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...
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
The human ability to learn diļ¬cult 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...
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...