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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
16 years 4 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 4 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
16 years 4 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
KDD
2006
ACM
113views Data Mining» more  KDD 2006»
16 years 4 months ago
A new efficient probabilistic model for mining labeled ordered trees
Mining frequent patterns is a general and important issue in data mining. Complex and unstructured (or semi-structured) datasets have appeared in major data mining applications, i...
Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhi...
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
16 years 4 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han