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TNN
2008
182views more  TNN 2008»
13 years 10 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
ICPR
2006
IEEE
14 years 11 months ago
Scalable Representative Instance Selection and Ranking
Finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one cons...
Xindong Wu, Xingquan Zhu
SAC
2005
ACM
14 years 3 months ago
Automatic extraction of informative blocks from webpages
Search engines crawl and index webpages depending upon their informative content. However, webpages — especially dynamically generated ones — contain items that cannot be clas...
Sandip Debnath, Prasenjit Mitra, C. Lee Giles
SDM
2008
SIAM
133views Data Mining» more  SDM 2008»
13 years 11 months ago
Semantic Smoothing for Bayesian Text Classification with Small Training Data
Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
Xiaohua Zhou, Xiaodan Zhang, Xiaohua Hu
KDD
2012
ACM
183views Data Mining» more  KDD 2012»
12 years 15 days ago
Mining discriminative components with low-rank and sparsity constraints for face recognition
This paper introduces a novel image decomposition approach for an ensemble of correlated images, using low-rank and sparsity constraints. Each image is decomposed as a combination...
Qiang Zhang, Baoxin Li