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SDM
2008
SIAM
133views Data Mining» more  SDM 2008»
14 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
DATAMINE
1999
108views more  DATAMINE 1999»
14 years 9 months ago
A Survey of Methods for Scaling Up Inductive Algorithms
Abstract. One of the de ning challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, ...
Foster J. Provost, Venkateswarlu Kolluri
ADMA
2010
Springer
271views Data Mining» more  ADMA 2010»
14 years 5 months ago
Exploiting Concept Clumping for Efficient Incremental E-Mail Categorization
We introduce a novel approach to incremental e-mail categorization based on identifying and exploiting "clumps" of messages that are classified similarly. Clumping reflec...
Alfred Krzywicki, Wayne Wobcke
KDD
2009
ACM
133views Data Mining» more  KDD 2009»
15 years 10 months ago
On the tradeoff between privacy and utility in data publishing
In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility o...
Tiancheng Li, Ninghui Li
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
15 years 4 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...