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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
VLDB
2009
ACM
159views Database» more  VLDB 2009»
16 years 4 months ago
Anytime measures for top-k algorithms on exact and fuzzy data sets
Top-k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this article, we initiate research on the anytime ...
Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Ni...
VLDB
2004
ACM
126views Database» more  VLDB 2004»
15 years 9 months ago
Database Challenges in the Integration of Biomedical Data Sets
The clinical and basic science research domains present exciting and difficult data integration issues. Solving these problems is crucial as current research efforts in the field ...
Rakesh Nagarajan, Mushtaq Ahmed, Aditya Phatak
IJON
2008
173views more  IJON 2008»
15 years 4 months ago
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitabl...
Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li
PAMI
2006
141views more  PAMI 2006»
15 years 4 months ago
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameter
We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...
Stéphane Lafon, Ann B. Lee