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» On learning algorithm selection for classification
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KDD
2004
ACM
187views Data Mining» more  KDD 2004»
16 years 7 months ago
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or la...
Jun Yan, Benyu Zhang, Shuicheng Yan, Qiang Yang, H...
JMLR
2008
133views more  JMLR 2008»
15 years 6 months ago
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classi...
Suhrid Balakrishnan, David Madigan
ICPR
2008
IEEE
16 years 1 months ago
An improvement on learning with local and global consistency
A modified version for semi-supervised learning algorithm with local and global consistency was proposed in this paper. The new method adds the label information, and adopts the g...
Jie Gui, De-Shuang Huang, Zhuhong You
PAMI
2006
114views more  PAMI 2006»
15 years 6 months ago
Nonparametric Supervised Learning by Linear Interpolation with Maximum Entropy
Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors" of...
Maya R. Gupta, Robert M. Gray, Richard A. Olshen
AAAI
1998
15 years 8 months ago
Learning to Classify Text from Labeled and Unlabeled Documents
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...