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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 11 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
14 years 9 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto
CORR
2010
Springer
193views Education» more  CORR 2010»
13 years 7 months ago
A Probabilistic Approach for Learning Folksonomies from Structured Data
Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach t...
Anon Plangprasopchok, Kristina Lerman, Lise Getoor
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
13 years 6 months ago
Active Learning from Multiple Noisy Labelers with Varied Costs
In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, ...
Yaling Zheng, Stephen D. Scott, Kun Deng
KDD
2000
ACM
162views Data Mining» more  KDD 2000»
14 years 6 days ago
Data Mining from Functional Brain Images
Recent advances in functional brain imaging enable identication of active areas of a brain performing a certain function. Induction of logical formulas describing relations betwee...
Mitsuru Kakimoto, Chie Morita, Yoshiaki Kikuchi, H...