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» Classification as Mining and Use of Labeled Itemsets
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KDD
2009
ACM
204views Data Mining» more  KDD 2009»
14 years 8 months ago
Improving classification accuracy using automatically extracted training data
Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parall...
Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, R...
SDM
2008
SIAM
114views Data Mining» more  SDM 2008»
13 years 8 months ago
Semi-Supervised Classification with Universum
The Universum data, defined as a collection of "nonexamples" that do not belong to any class of interest, have been shown to encode some prior knowledge by representing ...
Dan Zhang, Jingdong Wang, Fei Wang, Changshui Zhan...
ICDM
2003
IEEE
220views Data Mining» more  ICDM 2003»
14 years 20 days ago
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
Kang Peng, Slobodan Vucetic, Bo Han, Hongbo Xie, Z...
PAKDD
2004
ACM
96views Data Mining» more  PAKDD 2004»
14 years 22 days ago
Spectral Energy Minimization for Semi-supervised Learning
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-super...
Chun Hung Li, Zhi-Li Wu
KDD
2009
ACM
152views Data Mining» more  KDD 2009»
14 years 8 months ago
A multi-relational approach to spatial classification
Spatial classification is the task of learning models to predict class labels based on the features of entities as well as the spatial relationships to other entities and their fe...
Richard Frank, Martin Ester, Arno Knobbe