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CIARP
2003
Springer
9 years 7 months ago
Restricted Decontamination for the Imbalanced Training Sample Problem
Ricardo Barandela, E. Rangel, José Salvador...
TCBB
2011
8 years 9 months ago
Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification
—In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority classes. It is because the trained classifiers are mostly d...
Sangyoon Oh, Min Su Lee, Byoung-Tak Zhang
ICDM
2008
IEEE
110views Data Mining» more  ICDM 2008»
9 years 9 months ago
Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data
Class imbalance is a ubiquitous problem in supervised learning and has gained wide-scale attention in the literature. Perhaps the most prevalent solution is to apply sampling to t...
David A. Cieslak, Nitesh V. Chawla
ECAI
2006
Springer
9 years 6 months ago
Text Sampling and Re-Sampling for Imbalanced Authorship Identification Cases
Authorship identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candida...
Efstathios Stamatatos
JMLR
2010
139views more  JMLR 2010»
8 years 9 months ago
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Alternating Gibbs sampling is the most common scheme used for sampling from Restricted Boltzmann Machines (RBM), a crucial component in deep architectures such as Deep Belief Netw...
Guillaume Desjardins, Aaron C. Courville, Yoshua B...
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