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» Theoretical Frameworks for Data Mining
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KDD
2008
ACM
120views Data Mining» more  KDD 2008»
14 years 9 months ago
Multi-class cost-sensitive boosting with p-norm loss functions
We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establ...
Aurelie C. Lozano, Naoki Abe
WWW
2010
ACM
14 years 4 months ago
b-Bit minwise hashing
This paper establishes the theoretical framework of b-bit minwise hashing. The original minwise hashing method has become a standard technique for estimating set similarity (e.g.,...
Ping Li, Arnd Christian König
ICDM
2007
IEEE
122views Data Mining» more  ICDM 2007»
14 years 3 months ago
Representing Tuple and Attribute Uncertainty in Probabilistic Databases
There has been a recent surge in work in probabilistic databases, propelled in large part by the huge increase in noisy data sources — sensor data, experimental data, data from ...
Prithviraj Sen, Amol Deshpande, Lise Getoor
SDM
2010
SIAM
195views Data Mining» more  SDM 2010»
13 years 10 months ago
Adaptive Informative Sampling for Active Learning
Many approaches to active learning involve periodically training one classifier and choosing data points with the lowest confidence. An alternative approach is to periodically cho...
Zhenyu Lu, Xindong Wu, Josh Bongard
ICDM
2010
IEEE
109views Data Mining» more  ICDM 2010»
13 years 7 months ago
Term Filtering with Bounded Error
Abstract--In this paper, we consider a novel problem referred to as term filtering with bounded error to reduce the term (feature) space by eliminating terms without (or with bound...
Zi Yang, Wei Li, Jie Tang, Juanzi Li